Business activity management system

ABSTRACT

An automated method and system ( 100 ) for identifying and measuring the value and risk associated with tangible elements of value, intangible elements of value and external factors and using said measurements to optimize financial performance, risk transfer and business activities.

CROSS REFERENCE TO RELATED APPLICATIONS AND PATENTS

[0001] This application is a continutation of U.S. patent applicationSer. No. 09/688,983 filed Oct. 17, 2000 the disclosure of which isincorporated herein by reference. The subject matter of this applicationis also related to the subject matter of U.S. patent application Ser.No. 09/940,450 filed Aug. 29, 2001, U.S. patent application Ser. No.10/746,673 filed Dec. 24, 2003 and U.S. Pat. No. 5,615,109 for “Methodof and System for Generating Feasible, Profit Maximizing RequisitionSets”, by Jeff S. Eder, the disclosures of which are incorporated hereinby reference.

BACKGROUND OF THE INVENTION

[0002] This invention relates to a method of and system for identifyingand measuring the value and risk associated with tangible elements ofvalue, intangible elements of value and/or external factors and usingsaid measurements to optimize financial performance, risk transfer andassociated business activities for an organization.

SUMMARY OF THE INVENTION

[0003] It is a general object of the present invention to identify andmeasure the value and risk associated with tangible elements of value,intangible elements of value and external factors and using saidmeasurements to support the optimization of business activities for anorganization.

[0004] A preferable object to which the present invention is applied isquantifying and then satisfying the risk reduction needs for acommercial business. Comprehensive quantification of enterprisefinancial status is enabled by:

[0005] 1) Systematically analyzing and valuing contingent liabilitiesusing real option algorithms in order to ensure that the most currentinformation regarding the magnitude of potential liabilities is includedin all analyses;

[0006] 2) Developing an improved understanding of the variability andrisk associated with all elements of enterprise value—tangible andintangible;

[0007] 3) Incorporating insights from the analyses of performance byasset management systems (i.e. Customer Relationship Management, SupplyChain Management, Brand Management, etc.) and the analyses of risk byasset risk management systems (credit risk, currency risk, etc.) forindividual assets;

[0008] 4) Integrating or fusing the information from the first threeitems in order to develop a view of the risk and opportunities faced bythe company;

[0009] 5) Clearly identifying the liquidity and foreign exchangeposition of the enterprise; and

[0010] 6) Developing the optimal risk reduction program for the companywithin the constraints specified by the user.

[0011] The system of the present invention also calculates and displaysan optimal value enhancement program for the commercial enterprise usingthe system. Because information on liquidity and foreign exchange needsis developed and transmitted along with the risk information, the systemof the present invention is also capable of functioning as an automated,on-line liquidity transfer system, alone or in combination with the risktransfer system.

[0012] In addition to enabling the just in time provision of financialservices, the present invention has the added benefit of eliminating agreat deal of time-consuming and expensive effort by automating theextraction of data from the databases, tables, and files of existingcomputer-based corporate finance, operations, human resource, supplychain, web-site and “soft” asset management system databases in order tooperate the system. In accordance with the invention, the automatedextraction, aggregation and analysis of data from a variety of existingcomputer-based systems significantly increases the scale and scope ofthe analysis that can be completed. The system of the present inventionfurther enhances the efficiency and effectiveness of the businessvaluation by automating the retrieval, storage and analysis ofinformation useful for valuing elements of value from externaldatabases, external publications and the internet. Uncertainty overwhich method is being used for completing the valuation and theresulting inability to compare different valuations is eliminated by thepresent invention by consistently utilizing the same set of valuationmethodologies for valuing the different elements of enterprise value asshown in Table 1. TABLE 1 Enterprise Element of Value ValuationMethodology Excess Cash & Marketable Securities GAAP Market SentimentMarket Value* − (COPTOT + ΣReal option Values) Total current-operationvalue (COPTOT): Income Valuation Financial Assets: Cash & MarketableGAAP Securities (CASH) Financial Assets: Accounts Receivable GAAP (AR)Financial Assets: Inventory (IN) GAAP Financial Assets: Prepaid ExpensesGAAP (PE) Financial Assets: Other Assets (OA) Lower of GAAP orliquidation value Elements of Production If calculated value > Value:Equipment (PEQ) liquidation value, then use system calculated value,else use liqui- dation value Elements of Intangible Elements Systemcalculated Value: (IE): Customers, value for each IE Employees, Vendors,Strategic Partner- ships, Brands, Other Intangibles Elements of GeneralGoing GCV = COPTOT − Value: Concern Value CASH − AR − IN − (GCV) PE −PEQ − OA − ΣIE Real Options Real option algorithms & industry realoption allocation each based on relative strength of intangible elementsContingent Liabilities Real option algorithms

[0013] The market value of the enterprise is calculated by combining themarket value of all debt and equity as shown in Table2. TABLE 2Enterprise Market Value = Σ Market value of enterprise equity − Σ Marketvalue of company debt

[0014] Consultants from McKinsey & Company recently completed a threeyear study of companies in 10 industry segments in 12 countries thatconfirmed the importance of intangible elements of value as enablers ofnew business expansion and profitable growth. The results of the study,published in the book The Alchemy of Growth, revealed three commoncharacteristics of the most successful businesses in the currenteconomy:

[0015] 1. They consistently utilize “soft” or intangible assets likebrands, customers and employees to support business expansion;

[0016] 2. They systematically generate and harvest real options forgrowth; and

[0017] 3. Their management focuses on three distinct “horizons”—shortterm (1-3 years), growth (3-5 years out) and options (beyond 5 years).

[0018] The experience of several of the most important companies in theU.S. economy, e.g. IBM, General Motors and DEC, in the late 1980s andearly 1990s illustrate the problems that can arise when intangible assetinformation is omitted from corporate financial statements and companiesfocus only on the short term horizon. All three companies were showinglarge profits using current accounting systems while their businesseswere deteriorating. If they had been forced to take write-offs when thedeclines in intangible assets were occurring, the problems would havebeen visible to the market and management would have been forced to actto correct the problems much more quickly than they actually did. Thesedeficiencies of traditional accounting systems are particularlynoticeable in high technology companies that are highly valued for theirintangible assets and their options to enter growing markets rather thantheir tangible assets.

[0019] The utility of the valuations produced by the system of thepresent invention are further enhanced by explicitly calculating theexpected purchase longevity of the different customers and differentelements of value in order to improve the accuracy and usefulness of thevaluations.

[0020] As shown in Table 1, real options and contingent liabilities arevalued using real option algorithms. Because real option algorithmsexplicitly recognize whether or not an investment is reversible and/orif it can be delayed, the values calculated using these algorithms aremore realistic than valuations created using more traditional approacheslike Net Present Value. The use of real option analysis for valuinggrowth opportunities and contingent liabilities (hereinafter, realoptions) gives the present invention a distinct advantage overtraditional approaches to risk management.

[0021] The innovative system has the added benefit of providing a largeamount of detailed information to the enterprise and central exchangeusers concerning both tangible and intangible elements of value. Becauseintangible elements are by definition not tangible, they can not bemeasured directly. They must instead be measured by the impact they haveon their surrounding environment. There are analogies in the physicalworld. For example, electricity is an “intangible” that is measured bythe impact it has on the surrounding environment. Specifically, thestrength of the magnetic field generated by the flow of electricitythrough a conductor is used to determine the amount of electricity thatis being consumed. The system of the present invention measuresintangible elements of value by identifying the attributes that, likethe magnetic field, reflect the strength of the element in drivingcomponents of value (revenue, expense and change in capital), realoptions and market price for company equity and are easy to measure.Once the attributes related to the impact of each element areidentified, they can be summarized into a single expression (a compositevariable or vector). The vectors for all elements are then evaluatedusing a series of models to determine the relative contribution of eachelement to driving the components of value and market value. The systemof the present invention then calculates the product of the relativeelement contributions and the value of the components of value, realoptions and market value to determine the net value impact of eachelement (see Table 5).

[0022] The method for tracking all the elements of value for a businessenterprise provided by the present invention eliminates many of thelimitations associated with current systems for risk management thatwere described previously. To facilitate its use as a tool for managingrisk for a commercial enterprise, the system of the present inventionproduces reports in formats that are similar to the reports provided bytraditional accounting systems. Incorporating information regarding allthe elements of value is just one of the ways the system of the presentinvention overcomes the limitations of existing systems. Other advancesinclude:

[0023] 1. The integrated analysis of all risks,

[0024] 2. The automated analysis of both “normal” risks and extremerisks, and

[0025] 3. The preparation of an xml summary of enterprise risk thatenable the automated delivery of risk management products and services.

[0026] By eliminating many of the gaps in information available topersonnel in the enterprise and the central exchange, the system of thepresent invention enables the just-in-time provision of financialservices that are tailored to the exact needs of the enterprise. Theelectronic linkage also eliminates the time lag that prevents many fromcompanies from obtaining the risk reduction products they need

BRIEF DESCRIPTION OF DRAWINGS

[0027] These and other objects, features and advantages of the presentinvention will be more readily apparent from the following descriptionof one embodiment of the invention in which:

[0028]FIG. 1 is a block diagram showing the major processing steps ofthe present invention;

[0029]FIG. 2 is a diagram showing the files or tables in the applicationdatabase (50) of the

[0030] present invention that are utilized for data storage andretrieval during the processing in the innovative risk transfer system;

[0031]FIG. 3 is a block diagram of an implementation of the presentinvention;

[0032]FIG. 4 is a diagram showing the data windows that are used forreceiving information from and transmitting information to the user (20)during system processing;

[0033]FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E and FIG. 5F are blockdiagrams showing the sequence of steps in the present invention used forspecifying system settings and for initializing and operating the databots that extract, aggregate, store and manipulate information utilizedin system processing from: user input, the basic financial systemdatabase, the operation management system database, the web sitetransaction log database, the human resource information systemdatabase, risk management system database, external databases, theadvanced finance system database, soft asset management systemdatabases, the supply chain system database and the internet;

[0034]FIG. 6A, FIG. 6B and FIG. 6C are block diagrams showing thesequence of steps in the present invention that are utilized forinitializing and operating the analysis bots;

[0035]FIG. 7 is a block diagram showing the sequence of steps in thepresent invention used for developing the optimal risk reductionstrategy for each enterprise; and

[0036]FIG. 8 is a block diagram showing the sequence of steps in thepresent invention used in communicating the summary information,printing reports, and receiving information concerning swaps andcoverage from an exchange or other risk transfer provider (600).

DETAILED DESCRIPTION OF ONE EMBODIMENT

[0037]FIG. 1 provides an overview of the processing completed by theinnovative system for business activity management. Processing starts inthis system (100) with the specification of system settings and theinitialization and activation of software data “bots” (200) thatextract, aggregate, manipulate and store the data and user (20) inputrequired for completing system processing. This information is extractedvia a network (45) from: a basic financial system database (5), anoperation management system database (10), a web site transaction logdatabase (12), a human resource information system database (15), a riskmanagement system database (17), an external database (25), an advancedfinancial system database (30), a soft asset management system database(35), a supply chain system database (37) and the internet (40). Theseinformation extractions and aggregations may be influenced by a user(20) through interaction with a user-interface portion of theapplication software (700) that mediates the display, transmission andreceipt of all information to and from browser software (800) such asthe Microsoft Internet Explorer in an access device (90) such as a phoneor personal computer that the user (20) interacts with. While only onedatabase of each type (5, 10, 12, 15, 17, 25, 30, 35 and 37) is shown inFIG. 1, it is to be understood that the system (100) can extract datafrom multiple databases of each type via the network (45). It also to beunderstood that the user (20) and the exchange operator (21) can operateseparate access devices (90). One embodiment of the present inventioncontains a soft asset management system for each element of value beinganalyzed. Automating the extraction and analysis of data from each softasset management system ensures that each soft asset is consideredwithin the overall financial models for the enterprise. It should alsobe understood that it is possible to complete a bulk extraction of datafrom each database (5, 10, 12, 15, 17, 25, 30, 35 and 37) via thenetwork (45) using data extraction applications such as DataTransformation Services from Microsoft or Aclue from Decisionism beforeinitializing the data bots. The data extracted in bulk could be storedin a single datamart or data warehouse where the data bots could operateon the aggregated data.

[0038] All extracted information is stored in a file or table(hereinafter, table) within an application database (50) as shown inFIG. 2 or an exchange database (51) as shown in FIG. 10. The applicationdatabase (50) contains tables for storing user input, extractedinformation and system calculations including a system settings table(140), a metadata mapping table (141), a conversion rules table (142), abasic financial system table (143), an operation system table (144), ahuman resource system table (145), an external database table (146), anadvanced finance system table (147), a soft asset system table (148), abot date table (149), a keyword table (150), a classified text table(151), a geospatial measures table (152), a composite variables table(153), an industry ranking table (154), an element of value definitiontable (155), a component of value definition table (156), a cluster IDtable (157), an element variables table (158), a vector table (159), abot table (160), a cash flow table (161), a real option value table(162), an enterprise vector table (163), a report table (164), an riskreduction purchase table (165), an enterprise sentiment table (166), avalue driver change table (167), a simulation table (168), a sentimentfactors table (169), an statistics table (170), a scenarios table (171),a web log data table (172), a risk reduction products table (173), asupply chain system table (174), an optimal mix table (175), a risksystem table (176), an xml summary table (177), a generic risk table(178) and a risk reduction activity table (179). The applicationdatabase (50) can optionally exist as a datamart, data warehouse ordepartmental warehouse. The system of the present invention has theability to accept and store supplemental or primary data directly fromuser input, a data warehouse or other electronic files in addition toreceiving data from the databases described previously. The system ofthe present invention also has the ability to complete the necessarycalculations without receiving data from one or more of the specifieddatabases. However, in one embodiment all required information isobtained from the specified data sources (5, 10, 12,15,17, 25, 30, 35,37 and 40).

[0039] As shown in FIG. 3, one embodiment of the present invention is acomputer system (100) illustratively comprised of a user-interfacepersonal computer (110) connected to an application-server personalcomputer (120) via a network (45). The application server personalcomputer (120) is in turn connected via the network (45) to adatabase-server personal computer (130). The user interface personalcomputer (110) is also connected via the network (45) to an internetbrowser appliance (90) that contains browser software (800) such asMicrosoft Internet Explorer or Netscape Navigator.

[0040] The database-server personal computer (130) has a read/writerandom access memory (131), a hard drive (132) for storage of theapplication database (50), a keyboard (133), a communications bus (134),a display (135), a mouse (136), a CPU (137) and a printer (138).

[0041] While only one database server personal computer is shown in FIG.3, it is to be understood that the exchange-server personal computer(140) can be networked to one thousand or more database server personalcomputers (130) via the network (45).

[0042] The application-server personal computer (120) has a read/writerandom access memory (121), a hard drive (122) for storage of thenon-user-interface portion of the enterprise portion of the applicationsoftware (200, 300, 400 and 500) of the present invention, a keyboard(123), a communications bus (124), a display (125), a mouse (126), a CPU(127) and a printer (128). While only one client personal computer isshown in FIG. 3, it is to be understood that the application-serverpersonal computer (120) can be networked to fifty or more clientpersonal computers (110) via the network (45). The application-serverpersonal computer (120) can also be networked to fifty or more server,personal computers (130) via the network (45). It is to be understoodthat the diagram of FIG. 3 is merely illustrative of one embodiment ofthe present invention.

[0043] The user-interface personal computer (110) has a read/writerandom access memory (111), a hard drive (112) for storage of a clientdata-base (49) and the user-interface portion of the applicationsoftware (700), a keyboard (113), a communications bus (114), a display(115), a mouse (116), a CPU (117) and a printer (118).

[0044] The application software (200, 300, 400, 500 and 700) controlsthe performance of the central processing unit (127) as it completes thecalculations required to support the enterprise portion of the risktransfer system. In the embodiment illustrated herein, the applicationsoftware program (200, 300, 400, 500, 600 and 700) is written in acombination of C++ and Visual Basic®. The application software (200,300, 400, 500 and 700) can use Structured Query Language (SQL) forextracting data from the databases and the internet (5, 10, 12, 15, 17,25, 30, 35, 37 and 40). The user (20) can optionally interact with theuser-interface portion of the application software (700) using thebrowser software (800) in the browser appliance (90) to provideinformation to the application software (200, 300, 400, 500 and 700) foruse in determining which data will be extracted and transferred to theapplication database (50) by the data bots.

[0045] User input is initially saved to the client database (49) beforebeing transmitted to the communication bus (124) and on to the harddrive (122) of the application-server computer via the network (45).Following the program instructions of the application software, thecentral processing unit (127) accesses the extracted data and user inputby retrieving it from the hard drive (122) using the random accessmemory (121) as computation workspace in a manner that is well known.

[0046] The computers (110, 120 and 130) shown in FIG. 3 illustrativelyare IBM PCs or clones or any of the more powerful computers orworkstations that are widely available. Typical memory configurationsfor client personal computers (110) used with the present inventionshould include at least 512 megabytes of semiconductor random accessmemory (111) and at least a 100 gigabyte hard drive (112). Typicalmemory configurations for the application-server personal computer (120)used with the present invention should include at least 2056 megabytesof semiconductor random access memory (121) and at least a 250 gigabytehard drive (122). Typical memory configurations for the database-serverpersonal computer (130) used with the present invention should includeat least 4112 megabytes of semiconductor random access memory (131) andat least a 500 gigabyte hard drive (132). Typical memory configurationsfor the exchange-server personal computer (140) used with the presentinvention should include at least 8224 megabytes of semiconductor randomaccess memory (145) and at least a 750 gigabyte hard drive (141).

[0047] Using the system described above, enterprise activity isanalyzed, a comprehensive risk management program is developed andimplemented for each enterprise after element of value within theenterprise is analyzed using the formulas and data listed in Table 1. Asshown in Table 1, the value of the current-operation will be calculatedusing an income valuation. An integral part of most income valuationmodels is the calculation of the present value of the expected cashflows, income or profits associated with the current-operation. Thepresent value of a stream of cash flows is calculated by discounting thecash flows at a rate that reflects the risk associated with realizingthe cash flow. For example, the present value (PV) of a cash flow of tendollars ($10) per year for five (5) years would vary depending on therate used for discounting future cash flows as shown below. Discountrate = 25% PV = 10 + 10 + 10 + 10 + 10 = 26.89 {overscore (1.25)}{overscore ((1.25))}² {overscore ((1.25))}³ {overscore ((1.25))}⁴{overscore ((1.25))}⁵ Discount rate = 35% PV = 10 + 10 + 10 + 10 + 10 =22.20 {overscore (1.35)} {overscore ((1.35))}² {overscore ((1.35))}³{overscore ((1.35))}⁴ {overscore ((1.35))}⁵

[0048] One of the first steps in evaluating the elements ofcurrent-operation value is extracting the data required to completecalculations in accordance with the formula that defines the value ofthe current-operation as shown in Table 4. TABLE 4 Value ofcurrent-operation = (R) Value of forecast revenue from current-operation(positive) + (E) Value of forecast expense for current-operation(negative) + (C)* Value of current operation capital change forecast

[0049] The three components of current-operation value will be referredto as the revenue value (R), the expense value (E) and the capital value(C). Examination of the equation in Table 4 shows that there are threeways to increase the value of the current-operation—increase therevenue, decrease the expense or decrease the capital requirements(note: this statement ignores a fourth way to increase value—decreasethe interest rate used for discounting future cash flows).

[0050] In one embodiment, the revenue, expense and capital requirementforecasts for the current operation, the real options and the contingentliabilities are obtained from an advanced financial planning systemdatabase (30) derived from an advanced financial planning system similarto the one disclosed in U.S. Pat. No. 5,615,109. The extracted revenue,expense and capital requirement forecasts are used to calculate a cashflow for each period covered by the forecast for the enterprise bysubtracting the expense and change in capital for each period from therevenue for each period. A steady state forecast for future periods iscalculated after determining the steady state growth rate that best fitsthe calculated cash flow for the forecast time period. The steady stategrowth rate is used to calculate an extended cash flow forecast. Theextended cash flow forecast is used to determine the CompetitiveAdvantage Period (CAP) implicit in the enterprise market value.

[0051] While it is possible to use analysis bots to sub-divide each ofthe components of current operation value into a number ofsub-components for analysis, one embodiment has a pre-determined numberof sub-components for each component of value for the enterprise. Therevenue value is not subdivided. In one embodiment, the expense value issubdivided into five sub-components: the cost of raw materials, the costof manufacture or delivery of service, the cost of selling, the cost ofsupport and the cost of administration. The capital value is subdividedinto six sub-components: cash, non-cash financial assets, productionequipment, other assets (non financial, non production assets),financial liabilities and equity. The production equipment and equitysub-components are not used directly in evaluating the elements ofvalue.

[0052] The components and sub-components of current-operation value willbe used in valuing the elements and sub-elements of value. An element ofvalue will be defined as “an identifiable entity or group of items thatas a result of past transactions and other data has provided and/or isexpected to provide economic benefit to an enterprise”. An item will bedefined as a single member of the group that defines an element ofvalue. For example, an individual salesman would be an “item” in the“element of value” sales staff. The data associated with performance ofan individual item will be referred to as “item variables”.

[0053] Analysis bots are used to determine element of value lives andthe percentage of: the revenue value, the expense value, and the capitalvalue that are attributable to each element of value. The resultingvalues are then added together to determine the valuation for differentelements as shown by the example in Table 5. TABLE 5 Element Gross ValuePercentage Life/CAP* Net Value Revenue value = $120 M 20% 80% Value =$19.2 M Expense value = ($80 M) 10% 80% Value = ($6.4) M Capital value =($5 M)  5% 80% Value = ($0.2) M Total value = $35 M Net value for thiselement: Value = $12.6 M

[0054] The risk reduction program development using the approachoutlined above is completed in five distinct stages. As shown in FIG.5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E and FIG. 5F the first stage ofprocessing (block 200 from FIG. 1) programs bots to continually extract,aggregate, manipulate and store the data from user input and databasesand the internet (5, 10, 12, 15, 17, 25, 30, 35, 37 and 40) in order forthe analysis of business value. Bots are independent components of theapplication that have specific tasks to perform. As shown in FIG. 6A,FIG. 6B and FIG. 6C the second stage of processing (block 300 fromFIG. 1) programs analysis bots that continually:

[0055] 1. Identify the item variables, item performance indicators andcomposite variables for each element of value and sub-element of valuethat drive the components of value (revenue, expense and changes incapital) and the market price of company equity,

[0056] 2. Create vectors that summarize the performance of the itemvariables and item performance indicators for each element of value andsub-element of value,

[0057] 3. Determine the appropriate discount rate on the basis ofrelative causal element strength and value the enterprise real optionsand contingent liabilities;

[0058] 4. Determine the appropriate discount rate, value and allocatethe industry real options to the enterprise on the basis of relativecausal element strength;

[0059] 5. Determine the expected life of each element of value andsub-element of value;

[0060] 6. Calculate the enterprise current operation value and value therevenue, expense and capital components of said current operations usingthe information prepared in the previous stage of processing;

[0061] 7. Specify and optimize predictive models to determine therelationship between the vectors determined in step 2 and the revenue,expense and capital component values determined in step 6,

[0062] 8. Combine the results of the fifth, sixth and seventh stages ofprocessing to determine the value of each element and sub-element (asshown in Table 5); and

[0063] 9. Determine the causal factors for company stock price movement,calculate market sentiment and analyze the causes of market sentiment.

[0064] The third stage of processing (block 400 from FIG. 1) analyzesthe risks faced by the enterprise in normal and extreme conditions inorder to develop a comprehensive risk management program for theenterprise. The fourth stage of processing (block 500 from FIG. 1)implements the risk reduction program by communicating with theexchange, purchasing the required risk reduction and/or by updating softasset, finance and operation management systems to implement riskreduction programs. The fifth and final stage of processing (block 600from FIG. 1) analyzes the risks from all the enterprises using theexchange, sets prices and communicates with each enterprise in order tocomplete risk reduction program transactions.

System Setting and Data Bots

[0065] The flow diagrams in FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5Eand FIG. 5F detail the processing that is completed by the portion ofthe application software (200) that extracts, aggregates, transforms andstores the information required for system operation from the: basicfinancial system database (5), operation management system database(10), the web site transaction log database (12), human resourceinformation system database (15), risk management system database (17),external database (25), advanced financial system database (30), softasset management system database (35), the supply chain system database(37), the internet (40) and the user (20). A brief overview of thedifferent databases will be presented before reviewing each step ofprocessing completed by this portion (200) of the application software.

[0066] Corporate financial software systems are generally divided intotwo categories, basic and advanced. Advanced financial systems utilizeinformation from the basic financial systems to perform financialanalysis, financial planning and financial reporting functions.Virtually every commercial enterprise uses some type of basic financialsystem as they are required to use these systems to maintain books andrecords for income tax purposes. An increasingly large percentage ofthese basic financial systems are resident in microcomputer andworkstation systems. Basic financial systems include general-ledgeraccounting systems with associated accounts receivable, accountspayable, capital asset, inventory, invoicing, payroll and purchasingsubsystems. These systems incorporate worksheets, files, tables anddatabases. These databases, tables and files contain information aboutthe company operations and its related accounting transactions. As willbe detailed below, these databases, tables and files are accessed by theapplication software of the present invention in order to extract theinformation required for completing a business valuation. The system isalso capable of extracting the required information from a datawarehouse (or datamart) when the required information has beenpre-loaded into the warehouse.

[0067] General ledger accounting systems generally store only validaccounting transactions. As is well known, valid accounting transactionsconsist of a debit component and a credit component where the absolutevalue of the debit component is equal to the absolute value of thecredit component. The debits and the credits are posted to the separateaccounts maintained within the accounting system. Every basic accountingsystem has several different types of accounts. The effect that theposted debits and credits have on the different accounts depends on theaccount type as shown in Table 6. TABLE 6 Account Type: Debit Impact:Credit Impact: Asset Increase Decrease Revenue Decrease Increase ExpenseIncrease Decrease Liability Decrease Increase Equity Decrease Increase

[0068] General ledger accounting systems also require that the assetaccount balances equal the sum of the liability account balances andequity account balances at all times.

[0069] The general ledger system generally maintains summary, dollaronly transaction histories and balances for all accounts while theassociated subsystems, accounts payable, accounts receivable, inventory,invoicing, payroll and purchasing, maintain more detailed historicaltransaction data and balances for their respective accounts. It iscommon practice for each subsystem to maintain the detailed informationshown in Table 7 for each transaction. TABLE 7 Subsystem DetailedInformation Accounts Vendor, Item(s), Transaction Date, Amount Owed,Payable Due Date, Account Number Accounts Customer, Transaction Date,Product Sold, Receivable Quantity, Price, Amount Due, Terms, Due Date,Account Number Capital Asset ID, Asset Type, Date of Purchase, PurchaseAssets Price, Useful Life, Depreciation Schedule, Salvage ValueInventory Item Number, Transaction Date, Transaction Type, TransactionQty, Location, Account Number Invoicing Customer Name, Transaction Date,Product(s) Sold, Amount Due, Due Date, Account Number Payroll EmployeeName, Employee Title, Pay Frequency, Pay Rate, Account Number PurchasingVendor, Item(s), Purchase Quantity, Purchase Price(s), Due Date, AccountNumber

[0070] As is well known, the output from a general ledger systemincludes income statements, balance sheets and cash flow statements inwell defined formats which assist management in measuring the financialperformance of the firm during the prior periods when data input andsystem processing have been completed.

[0071] While basic financial systems are similar between firms,operation management systems vary widely depending on the type ofcompany they are supporting. These systems typically have the ability tonot only track historical transactions but to forecast futureperformance. For manufacturing firms, operation management systems suchas Enterprise Resource Planning Systems (ERP), Material RequirementPlanning Systems (MRP), Purchasing Systems, Scheduling Systems andQuality Control Systems are used to monitor, coordinate, track and planthe transformation of materials and labor into products. Systems similarto the one described above may also be useful for distributors to use inmonitoring the flow of products from a manufacturer.

[0072] Operation Management Systems in manufacturing firms may alsomonitor information relating to the production rates and the performanceof individual production workers, production lines, work centers,production teams and pieces of production equipment including theinformation shown in Table 8. TABLE 8 Operation Management System -Production Information 1. ID number (employee id/machine id) 2. Actualhours - last batch 3. Standard hours - last batch 4. Actual hours - yearto date 5. Actual/Standard hours - year to date % 6. Actual setup time -last batch 7. Standard setup time - last batch 8. Actual setup hours -year to date 9. Actual/Standard setup hrs - yr to date % 10. Cumulativetraining time 11. Job(s) certifications 12. Actual scrap - last batch13. Scrap allowance - last batch 14. Actual scrap/allowance - year todate 15. Rework time/unit last batch 16. Rework time/unit year to date17. QC rejection rate - batch 18. QC rejection rate - year to date

[0073] Operation management systems are also useful for trackingrequests for service to repair equipment in the field or in acentralized repair facility. Such systems generally store informationsimilar to that shown below in Table 9. TABLE 9 Operation ManagementSystem - Service Call Information 1. Customer name 2. Customer number 3.Contract number 4. Service call number 5. Time call received 6.Product(s) being fixed 7. Serial number of equipment 8. Name of personplacing call 9. Name of person accepting call 10. Promised response time11. Promised type of response 12. Time person dispatched to call 13.Name of person handling call 14. Time of arrival on site 15. Time ofrepair completion 16. Actual response type 17. Part(s) replaced 18.Part(s) repaired 19. 2nd call required 20. 2nd call number

[0074] Web site transaction log databases keep a detailed record ofevery visit to a web site, they can be used to trace the path of eachvisitor to the web site and upon further analysis can be used toidentify patterns that are most likely to result in purchases and thosethat are most likely to result in abandonment. This information can alsobe used to identify which promotion would generate the most value forthe company using the system. Web site transaction logs generallycontain the information shown in Table 10. TABLE 10 Web Site TransactionLog Database 1. Customer's URL 2. Date and time of visit 3. Pagesvisited 4. Length of page visit (time) 5. Type of browser used 6.Referring site 7. URL of site visited next 8. Downloaded file volume andtype 9. Cookies 10. Transactions

[0075] Computer based human resource systems may some times be packagedor bundled within enterprise resource planning systems such as thoseavailable from SAP, Oracle and Peoplesoft. Human resource systems areincreasingly used for storing and maintaining corporate recordsconcerning active employees in sales, operations and the otherfunctional specialties that exist within a modern corporation. Storingrecords in a centralized system facilitates timely, accurate reportingof overall manpower statistics to the corporate management groups andthe various government agencies that require periodic updates. In somecases human resource systems include the company payroll system as asubsystem. In one embodiment of the present invention, the payrollsystem is part of the basic financial system. These systems can also beused for detailed planning regarding future manpower requirements. Humanresource systems typically incorporate worksheets, files, tables anddatabases that contain information about the current and futureemployees. As will be detailed below, these databases, tables and filesare accessed by the application software of the present invention inorder to extract the information required for completing a businessvaluation. It is common practice for human resource systems to store theinformation shown in Table 11 for each employee. TABLE 11 Human ResourceSystem Information 1. Employee name 2. Job title 3. Job code 4. Rating5. Division 6. Department 7. Employee No./(Social Security Number) 8.Year to date - hours paid 9. Year to date - hours worked 10. Employeestart date - company 11. Employee start date - department 12. Employeestart date - current job 13. Training courses completed 14. Cumulativetraining expenditures 15. Salary history 16. Current salary 17.Educational background 18. Current supervisor

[0076] Risk management systems databases (17) contain statistical dataabout the past behavior and forecasts of likely future behavior ofinterest rates, currency exchange rates and commodity prices. They alsocontain information about the current mix of risk reduction products(derivatives, insurance, etc.) the enterprise has purchased. Somecompanies also use risk management systems to evaluate the desirabilityof extending or increasing credit lines to customers. The informationfrom these systems can be used to supplement the risk informationdeveloped by the system of the present invention.

[0077] External databases can be used for obtaining information thatenables the definition and evaluation of a variety of things includingelements of value, market value factors, industry real options andcomposite variables. In some cases information from these databases canbe used to supplement information obtained from the other databases andthe internet (5, 10, 12, 15, 17, 30, 35, 37 and 40). In the system ofthe present invention, the information extracted from external databases(25) can be in the forms listed in Table 12. TABLE 12 Types ofinformation 1) numeric information such as that found in the SEC Edgardatabase and the databases of financial infomediaries such as FirstCall,IBES and Compustat, 2) text information such as that found in the LexisNexis database and databases containing past issues from specificpublications, 3) risk management products such as derivatives andstandardized insurance contracts that can be purchased on line, 4)geospatial data; 5) multimedia information such as video and audioclips, and 6) generic risk data including information about thelikelihood of earthquake and weather damage by geospatial location

[0078] The system of the present invention uses different “bot” types toprocess each distinct data type from external databases (25). The same“bot types” are also used for extracting each of the different types ofdata from the internet (40). The system of the present invention musthave access to at least one external database (25) that providesinformation regarding the equity prices for the enterprise and theequity prices and financial performance of competitors.

[0079] Advanced financial systems may also use information from externaldatabases (25) and the internet (40) in completing their processing.Advanced financial systems include financial planning systems andactivity based costing systems. Activity based costing systems may beused to supplement or displace the operation of the expense componentanalysis segment of the present invention as disclosed previously.Financial planning systems generally use the same format used by basicfinancial systems in forecasting income statements, balance sheets andcash flow statements for future periods. Management uses the output fromfinancial planning systems to highlight future financial difficultieswith a lead time sufficient to permit effective corrective action and toidentify problems in company operations that may be reducing theprofitability of the business below desired levels. These systems aremost often developed by individuals within companies using two and threedimensional spreadsheets such as Lotus 1-2-3®, Microsoft Excel® andQuattro Pro®. In some cases, financial planning systems are built withinan executive information system (EIS) or decision support system (DSS).For one embodiment of the present invention, the advanced finance systemdatabase is similar to the financial planning system database detailedin U.S. Pat. No. 5,165,109 for “Method of and System for GeneratingFeasible, Profit Maximizing Requisition Sets”, by Jeff S. Eder, thedisclosure of which is incorporated herein by reference.

[0080] While advanced financial planning systems have been around forsome time, soft asset management systems are a relatively recentdevelopment. Their appearance is further proof of the increasingimportance of “soft” assets. Soft asset management systems include:alliance management systems, brand management systems, customerrelationship management systems, channel management systems,intellectual property management systems, process management systems andvendor management systems. Soft asset management systems are similar tooperation management systems in that they generally have the ability toforecast future events as well as track historical occurrences. Customerrelationship management systems are the most well established soft assetmanagement systems at this point and will be the focus of the discussionregarding soft asset management system data. In firms that sellcustomized products, the customer relationship management system isgenerally integrated with an estimating system that tracks the flow ofestimates into quotations, orders and eventually bills of lading andinvoices. In other firms that sell more standardized products, customerrelationship management systems generally are used to track the salesprocess from lead generation to lead qualification to sales call toproposal to acceptance (or rejection) and delivery. All customerrelationship management systems would be expected to track all of thecustomer's interactions with the enterprise after the first sale andstore information similar to that shown below in Table 13. TABLE 13Customer Relationship Management System - Information 1.Customer/Potential customer name 2. Customer number 3. Address 4. Phonenumber 5. Source of lead 6. Date of first purchase 7. Date of lastpurchase 8. Last sales call/contact 9. Sales call history 10. Salescontact history 11. Sales history: product/qty/price 12. Quotations:product/qty/price 13. Custom product percentage 14. Payment history 15.Current A/R balance 16. Average days to pay

[0081] Supply chain management system databases (37) contain informationthat may have been in operation management system databases (10) in thepast. These systems provide enhanced visibility into the availability ofgoods and promote improved coordination between customers and theirsuppliers. All supply chain management systems would be expected totrack all of the items ordered by the enterprise after the firstpurchase and store information similar to that shown below in Table 14.TABLE 14 Supply Chain Management System Information 1. Stock KeepingUnit (SKU) 2. Vendor 3. Total Quantity on Order 4. Total Quantity inTransit 5. Total Quantity on Back Order 6. Total Quantity in Inventory7. Quantity available today 8. Quantity available next 7 days 9.Quantity available next 30 days 10. Quantity available next 90 days 11.Quoted lead time 12. Actual average lead time

[0082] System processing of the information from the different databases(5, 10, 12, 15, 17, 25, 30, 35 and 37) and the internet (40) describedabove starts in a block 201, FIG. 5A, which immediately passesprocessing to a software block 202. The software in block 202 promptsthe user (20) via the system settings data window (701) to providesystem setting information. The system setting information entered bythe user (20) is transmitted via the network (45) back to theapplication server (120) where it is stored in the system settings table(140) in the application database (50) in a manner that is well known.The specific inputs the user (20) is asked to provide at this point inprocessing are shown in Table 15. TABLE 15 1. New run or structurerevision? 2. Continuous, If yes, frequency? (hourly, daily, weekly,monthly or quarterly) 3. Structure of enterprise (department, etc.) 4.Enterprise checklist 5. Base account structure 6. Metadata standard(XML, MS OIM, MDC) 7. Location of basic financial system database andmetadata 8. Location of advanced finance system database and metadata 9.Location of human resource information system database and metadata 10.Location of operation management system database and metadata 11.Location of soft asset management system databases and metadata 12.Location of external databases and metadata 13. Location of web sitetransaction log database and metadata 14. Location of supply chainmanagement system database and metadata 15. Location of risk managementsystem database and metadata 16. Location of account structure 17. Basecurrency 18. Location of database and metadata for equity information19. Location of database and metadata for debt information 20. Locationof database and metadata for tax rate information 21. Location ofdatabase and metadata for currency conversion rate information 22.Geospatial data? If yes, identity of geocoding service. 23. The maximumnumber of generations to be processed without improving fitness 24.Default clustering algorithm (selected from list) and maximum clusternumber 25. Amount of cash and marketable securities required for day today operations 26. Total cost of capital (weighted average cost ofequity, debt and risk capital) 27. Number of months a product isconsidered new after it is first produced 28. Enterprise industrysegments (SIC Code) 29. Primary competitors by industry segment 30.Management report types (text, graphic, both) 31. Default reports 32.Default Missing Data Procedure 33. Maximum time to wait for user input34. Maximum discount rate for new projects (real option valuation) 35.Maximum number of sub-elements 36. Maximum amount to be spent on riskreduction per year 37. Confidence interval for risk reduction programs38. On line account information for risk reduction products

[0083] The enterprise checklists are used by a “rules” engine (such asthe one available from Neuron Data) in block 202 to influence the numberand type of items with pre-defined metadata mapping for each category ofvalue. For example, if the checklists indicate that the enterprise isfocused on branded, consumer markets, then additional brand relatedfactors will be pre-defined for mapping. The application of these systemsettings will be further explained as part of the detailed explanationof the system operation.

[0084] The software in block 202 uses the current system date todetermine the time periods (months) that require data to complete thecurrent operation and the real option valuations. After the date rangeis calculated it is stored in the system settings table (140). In oneembodiment the valuation of the current operation by the system utilizesbasic financial, advanced financial, soft asset management, supplychain, web-site transaction, external database and human resource datafor the three year period before and the three year forecast periodafter the current date. The user (20) also has the option of specifyingthe data periods that will be used for completing system calculations.

[0085] After the storage of system setting data is complete, processingadvances to a software block 203. The software in block 203 prompts theuser (20) via the metadata and conversion rules window (702) to mapmetadata using the standard specified by the user (20) (XML, MicrosoftOpen Information Model or the Metadata Coalitions specification) fromthe basic financial system database (5), the operation management systemdatabase (10), the web site transaction log database (12), the humanresource information system database (15), the risk management systemdatabase (17), the external database (25), the advanced financial systemdatabase (30), the soft asset management system database (35) and thesupply chain system database (37) to the enterprise hierarchy stored inthe system settings table (140) and to the pre-specified fields in themetadata mapping table (141). Pre-specified fields in the metadatamapping table include, the revenue, expense and capital components andsub-components for the enterprise and pre-specified fields for expectedvalue drivers. Because the bulk of the information being extracted isfinancial information, the metadata mapping often takes the form ofspecifying the account number ranges that correspond to the differentfields in the metadata mapping table (141). Table 16 shows the baseaccount number structure that the account numbers in the other systemsmust align with. For example, using the structure shown below, therevenue component for the enterprise could be specified as enterprise01, any department number, accounts 400 to 499 (the revenue accountrange) with any sub-account. TABLE 16 Account Number 01 - 902 (any) -477- 86 (any) Segment Enterprise Department Account Sub-account SubgroupWorkstation Marketing Revenue Singapore Position 4 3 2 1

[0086] As part of the metadata mapping process, any database fields thatare not mapped to pre-specified fields are defined by the user (20) ascomponent of value elements of value or non-relevant attributes andumapped” in the metadata mapping table (141) to the corresponding fieldsin each database in a manner identical to that described above for thepre-specified fields. After all fields have been mapped to the metadatamapping table (141), the software in block 203 prompts the user (20) viathe metadata and conversion rules window (702) to provide conversionrules for each metadata field for each data source. Conversion ruleswill include information regarding currency conversions and conversionfor units of measure that may be required to accurately and consistentlyanalyze the data. The inputs from the user (20) regarding conversionrules are stored in the conversion rules table (142) in the applicationdatabase (50). When conversion rules have been stored for all fieldsfrom every data source, then processing advances to a software block204.

[0087] The software in block 204 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 212. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 207.

[0088] The software in block 207 checks the bot date table (149) anddeactivates any basic financial system data bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 207 then initializes data bots foreach field in the metadata mapping table (141) that mapped to the basicfinancial system database (5) in accordance with the frequency specifiedby user (20) in the system settings table (140). Bots are independentcomponents of the application that have specific tasks to perform. Inthe case of data acquisition bots, their tasks are to extract andconvert data from a specified source and then store it in a specifiedlocation. Each data bot initialized by software block 207 will store itsdata in the basic financial system table (143). Every data acquisitionbot for every data source contains the information shown in Table 17.TABLE 17 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. The data source location 3. Mapping information 4. Timingof extraction 5. Conversion rules (if any) 6. Storage Location (to allowfor tracking of source and destination events) 7. Creation date (date,hour, minute, second)

[0089] After the software in block 207 initializes all the bots for thebasic financial system database, processing advances to a block 208. Inblock 208, the bots extract and convert data in accordance with theirpreprogrammed instructions in accordance with the frequency specified byuser (20) in the system settings table (140). As each bot extracts andconverts data from the basic financial system database (5), processingadvances to a software block 209 before the bot completes data storage.The software in block 209 checks the basic financial system metadata tosee if all fields have been extracted. If the software in block 209finds no unmapped data fields, then the extracted, converted data arestored in the basic financial system table (143). Alternatively, ifthere are fields that haven't been extracted, then processing advancesto a block 211. The software in block 211 prompts the user (20) via themetadata and conversion rules window (702) to provide metadata andconversion rules for each new field. The information regarding the newmetadata and conversion rules is stored in the metadata mapping table(141) and conversion rules table (142) while the extracted, converteddata are stored in the basic financial system table (143). It is worthnoting at this point that the activation and operation of bots where allthe fields have been mapped to the application database (50) continues.Only bots with unmapped fields “wait” for user input before completingdata storage. The new metadata and conversion rule information will beused the next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing passeson to software block 212.

[0090] The software in block 212 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 228. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 221.

[0091] The software in block 221 checks the bot date table (149) anddeactivates any operation management system data bots with creationdates before the current system date and retrieves information from thesystem settings table (140), metadata mapping table (141) and conversionrules table (142). The software in block 221 then initializes data botsfor each field in the metadata mapping table (141) that mapped to theoperation management system database (10) in accordance with thefrequency specified by user (20) in the system settings table (140).Each data bot initialized by software block 221 will store its data inthe operation system table (144).

[0092] After the software in block 221 initializes all the bots for theoperation management system database, processing advances to a block222. In block 222, the bots extract and convert data in accordance withtheir preprogrammed instructions in accordance with the frequencyspecified by user (20) in the system settings table (140). As each botextracts and converts data from the operation management system database(10), processing advances to a software block 209 before the botcompletes data storage. The software in block 209 checks the operationmanagement system metadata to see if all fields have been extracted. Ifthe software in block 209 finds no unmapped data fields, then theextracted, converted data are stored in the operation system table(144). Alternatively, if there are fields that haven't been extracted,then processing advances to a block 211. The software in block 211prompts the user (20) via the metadata and conversion rules window (702)to provide metadata and conversion rules for each new field. Theinformation regarding the new metadata and conversion rules is stored inthe metadata mapping table (141) and conversion rules table (142) whilethe extracted, converted data are stored in the operation system table(144). It is worth noting at this point that the activation andoperation of bots where all the fields have been mapped to theapplication database (50) continues. Only bots with unmapped fields“wait” for user input before completing data storage. The new metadataand conversion rule information will be used the next time bots areinitialized in accordance with the frequency established by the user(20). In either event, system processing then passes on to a softwareblock 225.

[0093] The software in block 225 checks the bot date table (149) anddeactivates any web site transaction log data bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 225 then initializes data bots foreach field in the metadata mapping table (141) that mapped to the website transaction log database (12) in accordance with the frequencyspecified by user (20) in the system settings table (140). Each data botinitialized by software block 225 will store its data in the web logdata table (172).

[0094] After the software in block 225 initializes all the bots for theweb site transaction log database, the bots extract and convert data inaccordance with their preprogrammed instructions in accordance with thefrequency specified by user (20) in the system settings table (140). Aseach bot extracts and converts data from the web site transaction logdatabase (12), processing advances to a software block 209 before thebot completes data storage. The software in block 209 checks the website transaction log metadata to see if all fields have been extracted.If the software in block 209 finds no unmapped data fields, then theextracted, converted data are stored in the web log data table (172).Alternatively, if there are fields that haven't been extracted, thenprocessing advances to a block 211. The software in block 211 promptsthe user (20) via the metadata and conversion rules window (702) toprovide metadata and conversion rules for each new field. Theinformation regarding the new metadata and conversion rules is stored inthe metadata mapping table (141) and conversion rules table (142) whilethe extracted, converted data are stored in the web log data table(172). It is worth noting at this point that the activation andoperation of bots where all the fields have been mapped to theapplication database (50) continues. Only bots with unmapped fields“wait” for user input before completing data storage. The new metadataand conversion rule information will be used the next time bots areinitialized in accordance with the frequency established by the user(20). In either event, system processing then passes on to a softwareblock 226.

[0095] The software in block 226 checks the bot date table (149) anddeactivates any human resource information system data bots withcreation dates before the current system date and retrieves informationfrom the system settings table (140), metadata mapping table (141) andconversion rules table (142). The software in block 226 then initializesdata bots for each field in the metadata mapping table (141) that mappedto the human resource information system database (15) in accordancewith the frequency specified by user (20) in the system settings table(140). Each data bot initialized by software block 226 will store itsdata in the human resource system table (145).

[0096] After the software in block 226 initializes all the bots for thehuman resource information system database, the bots extract and convertdata in accordance with their preprogrammed instructions in accordancewith the frequency specified by user (20) in the system settings table(140). As each bot extracts and converts data from the human resourceinformation system database (15), processing advances to a softwareblock 209 before the bot completes data storage. The software in block209 checks the human resource information system metadata to see if allfields have been extracted. If the software in block 209 finds nounmapped data fields, then the extracted, converted data are stored inthe human resource system table (145). Alternatively, if there arefields that haven't been extracted, then processing advances to a block211. The software in block 211 prompts the user (20).via the metadataand conversion rules window (702) to provide metadata and conversionrules for each new field. The information regarding the new metadata andconversion rules is stored in the metadata mapping table (141) andconversion rules table (142) while the extracted, converted data arestored in the human resource system table (145). It is worth noting atthis point that the activation and operation of bots where all thefields have been mapped to the application database (50) continues. Onlybots with unmapped fields “wait” for user input before completing datastorage. The new metadata and conversion rule information will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to software block 228.

[0097] The software in block 228 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 248. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 241.

[0098] The software in block 241 checks the bot date table (149) anddeactivates any external database data bots with creation dates beforethe current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 241 then initializes data bots foreach field in the metadata mapping table (141) that mapped to theexternal database (25) in accordance with the frequency specified byuser (20) in the system settings table (140). Each data bot initializedby software block 241 will store its data in the external database table(146).

[0099] After the software in block 241 initializes all the bots for theexternal database, processing advances to a block 242. In block 242, thebots extract and convert data in accordance with their preprogrammedinstructions. As each bot extracts and converts data from the externaldatabase (25), processing advances to a software block 209 before thebot completes data storage. The software in block 209 checks theexternal database metadata to see if all fields have been extracted. Ifthe software in block 209 finds no unmapped data fields, then theextracted, converted data are stored in the external database table(146). Alternatively, if there are fields that haven't been extracted,then processing advances to a block 211. The software in block 211prompts the user (20) via the metadata and conversion rules window (702)to provide metadata and conversion rules for each new field. Theinformation regarding the new metadata and conversion rules is stored inthe metadata mapping table (141) and conversion rules table (142) whilethe extracted, converted data are stored in the external database table(146). It is worth noting at this point that the activation andoperation of bots where all the fields have been mapped to theapplication database (50) continues. Only bots with unmapped fields“wait” for user input before completing data storage. The new metadataand conversion rule information will be used the next time bots areinitialized in accordance with the frequency established by the user(20). In either event, system processing then passes on to a softwareblock 245.

[0100] The software in block 245 checks the bot date table (149) anddeactivates any advanced financial system data bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 245 then initializes data bots foreach field in the metadata mapping table (141) that mapped to theadvanced financial system database (30) in accordance with the frequencyspecified by user (20) in the system settings table (140). Each data botinitialized by software block 245 will store its data in the advancedfinance system database table (147).

[0101] After the software in block 245 initializes all the bots for theadvanced finance system database, the bots extract and convert data inaccordance with their preprogrammed instructions in accordance with thefrequency specified by user (20) in the system settings table (140). Aseach bot extracts and converts data from the advanced financial systemdatabase (30), processing advances to a software block 209 before thebot completes data storage. The software in block 209 checks theadvanced finance system database metadata to see if all fields have beenextracted. If the software in block 209 finds no unmapped data fields,then the extracted, converted data are stored in the advanced financesystem database table (147). Alternatively, if there are fields thathaven't been extracted, then processing advances to a block 211. Thesoftware in block 211 prompts the user (20) via the metadata andconversion rules window (702) to provide metadata and conversion rulesfor each new field. The information regarding the new metadata andconversion rules is stored in the metadata mapping table (141) andconversion rules table (142) while the extracted, converted data arestored in the advanced finance system database table (147). It is worthnoting at this point that the activation and operation of bots where allthe fields have been mapped to the application database (50) continues.Only bots with unmapped fields “wait” for user input before completingdata storage. The new metadata and conversion rule information will beused the next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to software block 246.

[0102] The software in block 246 checks the bot date table (149) anddeactivates any soft asset management system data bots with creationdates before the current system date and retrieves information from thesystem settings table (140), metadata mapping table (141) and conversionrules table (142). The software in block 246 then initializes data botsfor each field in the metadata mapping table (141) that mapped to a softasset management system database (35) in accordance with the frequencyspecified by user (20) in the system settings table (140). Extractingdata from each soft asset management system ensures that the managementof each soft asset is considered and prioritized within the overallfinancial models for the each enterprise. Each data bot initialized bysoftware block 246 will store its data in the soft asset system table(148).

[0103] After the software in block 246 initializes bots for all softasset management system databases, the bots extract and convert data inaccordance with their preprogrammed instructions in accordance with thefrequency specified by user (20) in the system settings table (140). Aseach bot extracts and converts data from the soft asset managementsystem databases (35), processing advances to a software block 209before the bot completes data storage. The software in block 209 checksthe metadata for the soft asset management system databases to see ifall fields have been extracted. If the software in block 209 finds nounmapped data fields, then the extracted, converted data are stored inthe soft asset system table (148). Alternatively, if there are fieldsthat haven't been extracted, then processing advances to a block 211.The software in block 211 prompts the user (20) via the metadata andconversion rules window (702) to provide metadata and conversion rulesfor each new field. The information regarding the new metadata andconversion rules is stored in the metadata mapping table (141) andconversion rules table (142) while the extracted, converted data arestored in the soft asset system table (148). It is worth noting at thispoint that the activation and operation of bots where all the fieldshave been mapped to the application database (50) continues. Only botswith unmapped fields “wait” for user input before completing datastorage. The new metadata and conversion rule information will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to software block 248.

[0104] The software in block 248 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 264. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 261.

[0105] The software in block 261 checks the bot date table (149) anddeactivates any risk management system data bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 261 then initializes data bots foreach field in the metadata mapping table (141) that mapped to a riskmanagement system database (17) in accordance with the frequencyspecified by user (20) in the system settings table (140). Each data botinitialized by software block 261 will store its data in the risk systemtable (176).

[0106] After the software in block 261 initializes bots for all riskmanagement system databases, the bots extract and convert data inaccordance with their preprogrammed instructions in accordance with thefrequency specified by user (20) in the system settings table (140). Aseach bot extracts and converts data from the risk management systemdatabases (17), processing advances to a software block 209 before thebot completes data storage. The software in block 209 checks themetadata for the risk management system database (17) to see if allfields have been extracted. If the software in block 209 finds nounmapped data fields, then the extracted, converted data are stored inthe risk management system table (174). Alternatively, if there arefields that haven't been extracted, then processing advances to a block211. The software in block 211 prompts the user (20) via the metadataand conversion rules window (702) to provide metadata and conversionrules for each new field. The information regarding the new metadata andconversion rules is stored in the metadata mapping table (141) andconversion rules table (142) while the extracted, converted data arestored in the risk management system table (174). It is worth noting atthis point that the activation and operation of bots where all thefields have been mapped to the application database (50) continues. Onlybots with unmapped fields “wait” for user input before completing datastorage. The new metadata and conversion rule information will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to software block 262.

[0107] The software in block 262 checks the bot date table (149) anddeactivates any supply chain system data bots with creation dates beforethe current system date and retrieves information from the systemsettings table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 262 then initializes data bots foreach field in the metadata mapping table (141) that mapped to a supplychain system database (37) in accordance with the frequency specified byuser (20) in the system settings table (140). Each data bot initializedby software block 262 will store its data in the supply chain systemtable (174).

[0108] After the software in block 262 initializes bots for all supplychain system databases, the bots extract and convert data in accordancewith their preprogrammed instructions in accordance with the frequencyspecified by user (20) in the system settings table (140). As each botextracts and converts data from the supply chain system databases (37),processing advances to a software block 209 before the bot completesdata storage. The software in block 209 checks the metadata for thesupply chain system database (37) to see if all fields have beenextracted. If the software in block 209 finds no unmapped data fields,then the extracted, converted data are stored in the supply chain systemtable (174). Alternatively, if there are fields that haven't beenextracted, then processing advances to a block 211. The software inblock 211 prompts the user (20) via the metadata and conversion ruleswindow (702) to provide metadata and conversion rules for each newfield. The information regarding the new metadata and conversion rulesis stored in the metadata mapping table (141) and conversion rules table(142) while the extracted, converted data are stored in the supply chainsystem table (174). It is worth noting at this point that the activationand operation of bots where all the fields have been mapped to theapplication database (50) continues. Only bots with unmapped fields“wait” for user input before completing data storage. The new metadataand conversion rule information will be used the next time bots areinitialized in accordance with the frequency established by the user(20). In either event, system processing then passes on to softwareblock 264.

[0109] The software in block 264 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 276. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 265.

[0110] The software in block 265 prompts the user (20) via theidentification and classification rules window (703) to identifykeywords such as company names, brands, trademarks and competitors forpre-specified fields in the metadata mapping table (141). The user (20)also has the option of mapping keywords to other fields in the metadatamapping table (141). After specifying the keywords, the user (20) isprompted to select and classify descriptive terms for each keyword. Theinput from the user (20) is stored in the keyword table (150) in theapplication database before processing advances to a software block 267.

[0111] The software in block 267 checks the bot date table (149) anddeactivates any internet text and linkage bots with creation datesbefore the current system date and retrieves information from the systemsettings table (140), the metadata mapping table (141) and the keywordtable (150). The software in block 267 then initializes internet textand linkage bots for each field in the metadata mapping table (141) thatmapped to a keyword in accordance with the frequency specified by user(20) in the system settings table (140).

[0112] Bots are independent components of the application that havespecific tasks to perform. In the case of text and linkage bots, theirtasks are to locate, count and classify keyword matches and linkagesfrom a specified source and then store their findings in a specifiedlocation. Each text and linkage bot initialized by software block 267will store the location, count and classification data it discovers inthe classified text table (151). Multimedia data can be processed usingbots with essentially the same specifications if software to translateand parse the multimedia content is included in each bot. Every internettext and linkage bot contains the information shown in Table 18. TABLE18 1. Unique ID number (based on date, hour, minute, second of creation)2. Creation date (date, hour, minute, second) 3. Storage location 4.Mapping information 5. Home URL 6. Keyword 7. Descriptive term 1 To 7 +n. Descriptive term n

[0113] After being initialized, the text and linkage bots locate andclassify data from the internet (40) in accordance with their programmedinstructions with the frequency specified by user (20) in the systemsettings table (140). As each text bot locates and classifies data fromthe internet (40) processing advances to a software block 268 before thebot completes data storage. The software in block 268 checks to see ifall linkages are identified and all keyword hits are associated withdescriptive terms that have been classified. If the software in block268 doesn't find any unclassified “hits” or “links”, then the address,counts and classified text are stored in the classified text table(151). Alternatively, if there are terms that haven't been classified orlinks that haven't been identified, then processing advances to a block269. The software in block 269 prompts the user (20) via theidentification and classification rules window (703) to provideclassification rules for each new term. The information regarding thenew classification rules is stored in the keyword table (150) while thenewly classified text and linkages are stored in the classified texttable (151). It is worth noting at this point that the activation andoperation of bots where all fields map to the application database (50)continues. Only bots with unclassified fields will “wait” for user inputbefore completing data storage. The new classification rules will beused the next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to a software block 270.

[0114] The software in block 270 checks the bot date table (149) anddeactivates any external database bots with creation dates before thecurrent system date and retrieves information from the system settingstable (140), the metadata mapping table (141) and the keyword table(150). The software in block 270 then initializes external database botsfor each field in the metadata mapping table (141) that mapped to akeyword in accordance with the frequency specified by user (20) in thesystem settings table (140). Every bot initialized by software block 270will store the location, count and classification of data it discoversin the classified text table (151). Every external database bot containsthe information shown in Table 19. TABLE 19 1. Unique ID number (basedon date, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Storage location 4. Mapping information 5. Datasource 6. Keyword 7. Storage location 8. Descriptive term 1 To 8 + n.Descriptive term n

[0115] After being initialized the bots locate data from the externaldatabase (25) in accordance with its programmed instructions with thefrequency specified by user (20) in the system settings table (140). Aseach bot locates and classifies data from the external database (25)processing advances to a software block 268 before the bot completesdata storage. The software in block 268 checks to see if all keywordhits are associated with descriptive terms that have been classified. Ifthe software in block 268 doesn't find any unclassified units”, then theaddress, count and classified text are stored in the classified texttable (151) or the external database table (146) as appropriate.Alternatively, if there are terms that haven't been classified, thenprocessing advances to a block 269. The software in block 269 promptsthe user (20) via the identification and classification rules window(703) to provide classification rules for each new term. The informationregarding the new classification rules is stored in the keyword table(150) while the newly classified text is stored in the classified texttable (151). It is worth noting at this point that the activation andoperation of bots where all fields map to the application database (50)continues. Only bots with unclassified fields “wait” for user inputbefore completing data storage. The new classification rules will beused the next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to software block 276.

[0116] The software in block 276 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 291. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 277.

[0117] The software in block 277 checks the system settings table (140)to see if there is geocoded data in the application database (50) and todetermine which on-line geocoding service (Centrus™ from QM Soft orMapMarker™ from MapInfo) is being used. If geospatial data are not beingused, then processing advances to a block 291. Alternatively, if thesoftware in block 277 determines that geospatial data are being used,processing advances to a software block 278.

[0118] The software in block 278 prompts the user (20) via thegeospatial measure definitions window (709) to define the measures thatwill be used in evaluating the elements of value. After specifying themeasures, the user (20) is prompted to select the geospatial locus foreach measure from the data already stored in the application database(50). The input from the user (20) is stored in the geospatial measurestable (152) in the application database before processing advances to asoftware block 279.

[0119] The software in block 279 checks the bot date table (149) anddeactivates any geospatial bots with creation dates before the currentsystem date and retrieves information from the system settings table(140), the metadata mapping table (141) and the geospatial measurestable (152). The software in block 279 then initializes geospatial botsfor each field in the metadata mapping table (141) that mapped togeospatial data in the application database (50) in accordance with thefrequency specified by user (20) in the system settings table (140)before advancing processing to a software block 280.

[0120] Bots are independent components of the application that havespecific tasks to perform. In the case of geospatial bots, their tasksare to calculate user specified measures using a specified geocodingservice and then store the measures in a specified location. Eachgeospatial bot initialized by software block 279 will store the measuresit calculates in the application database table where the geospatialdata was found. Tables that could include geospatial data include: thebasic financial system table (143), the operation system table (144),the human resource system table (145), the external database table(146), the advanced finance system table (147) and the soft asset systemtable (148). Every geospatial bot contains the information shown inTable 20. TABLE 20 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Geospatial locus 6.Geospatial measure 7. Geocoding service

[0121] In block 280 the geospatial bots locate data and completemeasurements in accordance with their programmed instructions with thefrequency specified by the user (20) in the system settings table (140).As each geospatial bot retrieves data and calculates the geospatialmeasures that have been specified, processing advances to a block 281before the bot completes data storage. The software in block 281 checksto see if all geospatial data located by the bot has been measured. Ifthe software in block 281 doesn't find any unmeasured data, then themeasurement is stored in the application database (50). Alternatively,if there are data elements that haven't been measured, then processingadvances to a block 282. The software in block 282 prompts the user (20)via the geospatial measure definition window (709) to providemeasurement rules for each new term. The information regarding the newmeasurement rules is stored in the geospatial measures table (152) whilethe newly calculated measurement is stored in the appropriate table inthe application database (50). It is worth noting at this point that theactivation and operation of bots that don't have unmeasured fieldscontinues. Only the bots with unmeasured fields “wait” for user inputbefore completing data storage. The new measurement rules will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to a software block 291.

[0122] The software in block 291 checks: the basic financial systemtable (143), the operation system table (144), the human resource systemtable (145), the external database table (146), the advanced financesystem table (147), the soft asset system table (148), the classifiedtext table (151) and the geospatial measures table (152) to see if dataare missing from any of the periods required for system calculation. Therange of required dates was previously calculated by the software inblock 202. If there are no data missing from any period, then processingadvances to a software block 293. Alternatively, if there are missingdata for any field for any period, then processing advances to a block292.

[0123] The software in block 292, prompts the user (20) via the missingdata window (704) to specify the method to be used for filling theblanks for each item that is missing data. Options the user (20) canchoose from for filling the blanks include: the average value for theitem over the entire time period, the average value for the item over aspecified period, zero, the average of the preceding item and thefollowing item values and direct user input for each missing item. Ifthe user (20) doesn't provide input within a specified interval, thenthe default missing data procedure specified in the system settingstable (140) is used. When all the blanks have been filled and stored forall of the missing data, system processing advances to a block 293.

[0124] The software in block 293 calculates attributes by item for eachnumeric data field in the basic financial system table (143), theoperation system table (144), the human resource system table (145), theexternal database table (146), the advanced finance system table (147)and the soft asset system table (148). The attributes calculated in thisstep include: cumulative total value, the period-to-period rate ofchange in value, the rolling average value and a series of time laggedvalues. In a similar fashion the software in block 293 calculatesattributes for each date field in the specified tables including timesince last occurrence, cumulative time since first occurrence, averagefrequency of occurrence and the rolling average frequency of occurrence.The numbers derived from numeric and date fields are collectivelyreferred to as “item performance indicators”. The software in block 293also calculates pre-specified combinations of variables called compositevariables for measuring the strength of the different elements of value.The item performance indicators are stored in the table where the itemsource data was obtained and the composite variables are stored in thecomposite variables table (153) before processing advances to a block294.

[0125] The software in block 294 uses attribute derivation algorithmssuch as the AQ program to create combinations of the variables thatweren't pre-specified for combination. While the AQ program is used inone embodiment of the present: invention, other attribute derivationalgorithms, such as the LINUS algorithms, may be used to the sameeffect. The software creates these attributes using both item variablesthat were specified as 37 element” variables and item variables thatwere not. The resulting composite variables are stored in the compositevariables table (153) before processing advances to a block 295.

[0126] The software in block 295 derives market value factors byenterprise for each numeric data field with data in the sentimentfactors table (169). Market value factors include: the ratio ofenterprise earnings to expected earnings, commodity prices not capturedin process valuations, inflation rate, growth in g.d.p., volatility,volatility vs. industry average volatility, interest rates, increases ininterest rates, insider trading direction and levels, consumerconfidence and the unemployment rate that have an impact on the marketprice of the equity for an enterprise and/or an industry. The marketvalue factors derived in this step include: cumulative totals, theperiod to period rate of change, the rolling average value and a seriesof time lagged values. In a similar fashion the software in block 295calculates market value factors for each date field in the specifiedtable including time since last occurrence, cumulative time since firstoccurrence, average frequency of occurrence and the rolling averagefrequency of occurrence. The numbers derived from numeric and datefields are collectively referred to as “market performance indicators”.The software in block 295 also calculates pre-specified combinations ofvariables called composite factors for measuring the strength of thedifferent market value factors. The market performance indicators andthe composite factors are stored in the sentiment factors table (169)before processing advances to a block 296.

[0127] The software in block 296 uses attribute derivation algorithms,such as the Linus algorithm, to create combinations of the factors thatwere not pre-specified for combination. While the Linus algorithm isused in one embodiment of the present invention, other attributederivation algorithms, such as the AQ program, may be used to the sameeffect. The software creates these attributes using both market valuefactors that were included in “composite factors” and market valuefactors that were not. The resulting composite variables are stored inthe sentiment factors table (169) before processing advances to a block297.

[0128] The software in block 297 uses pattern-matching algorithms toassign pre-designated data fields for different elements of value topre-defined groups with numerical values. This type of analysis isuseful in classifying purchasing patterns and/or communications patternsas “heavy”, “light”, “moderate” or “sporadic”. The classification andthe numeric value associated with the classification are stored in theapplication database (50) table where the data field is located beforeprocessing advances to a block 298.

[0129] The software in block 298 retrieves data from the metadatamapping table (141), creates and then stores the definitions for thepre-defined components of value in the components of value definitiontable (155). As discussed previously, the revenue component of value isnot divided into sub-components, the expense value is divided into fivesub-components: the cost of raw materials, the cost of manufacture ordelivery of service, the cost of selling, the cost of support and thecost of administration and the capital value is divided into sixsub-components: cash, non-cash financial assets, production equipment,other assets, financial liabilities and equity in one embodiment.Different subdivisions of the components of value can be used to thesame effect. When data storage is complete, processing advances to asoftware block 302 to begin the analysis of the extracted data usinganalysis bots.

Analysis Bots

[0130] The flow diagrams in FIG. 6A, FIG. 6B and FIG. 6C detail theprocessing that is completed by the portion of the application software(300) that programs analysis bots to:

[0131] 1. Identify the item variables, item performance indicators andcomposite variables for each enterprise, element of value andsub-element of value that drive the components of value (revenue,expense and changes in capital),

[0132] 2. Create vectors that use item variables, item performanceindicators and composite variables to summarize the performance of eachenterprise, element of value and sub-element of value,

[0133] 3. Determine the causal factors for industry value, determine theappropriate interest rate, value and allocate the industry real optionsto each enterprise on the basis of relative element strength;

[0134] 4. Determine the appropriate interest rate on the basis ofrelative causal element strength and value the enterprise real options;

[0135] 5. Determine the expected life of each element of value andsub-element of value;

[0136] 6. Calculate the enterprise current operation value and value therevenue, expense and capital components using the information preparedin the previous stage of processing;

[0137] 7. Specify and optimize predictive causal models to determine therelationship between the vectors determined in step 2 and the revenue,expense and capital values determined in step 6,

[0138] 8. Combine the results of the fifth, sixth and seventh stages ofprocessing to determine the value of each, enterprise contribution,element and sub-element (as shown in Table 5);

[0139] 9. Calculate the market sentiment by subtracting the currentoperation value, the total value of real options and the allocatedindustry options from market value for the enterprise (if it has apublic stock market price); and

[0140] 10. Analyze the sources of market sentiment.

[0141] Each analysis bot generally normalizes the data being analyzedbefore processing begins.

[0142] Processing in this portion of the application begins in softwareblock 302. The software in block 302 checks the system settings table(140) in the application database (50) to determine if the currentcalculation is a new calculation or a structure change. If thecalculation is not a new calculation or a structure change thenprocessing advances to a software block 314. Alternatively, if thecalculation is new or a structure change, then processing advances to asoftware block 303.

[0143] The software in block 303 retrieves data from the meta datamapping table (141) and the soft asset system table (148) and thenassigns item variables, item performance indicators and compositevariables to each element of value using a two-step process. First, itemvariables and item performance indicators are assigned to elements ofvalue based on the soft asset management system they correspond to (forexample, all item variables from a brand management system and all itemperformance indicators derived from brand management system variablesare assigned to the brand element of value). Second, pre-definedcomposite variables are assigned to the element of value they wereassigned to measure in the metadata mapping table (141). After theassignment of variables and indicators to elements is complete, theresulting assignments are saved to the element of value definition table(155) and processing advances to a block 304.

[0144] The software in block 304 checks the bot date table (149) anddeactivates any temporal clustering bots with creation dates before thecurrent system date. The software in block 304 then initializes bots inorder for each component of value. The bots: activate in accordance withthe frequency specified by the user (20) in the system settings table(140), retrieve the information from the system settings table (140),the metadata mapping table (141) and the component of value definitiontable (156) in order and define segments for the component of value databefore saving the resulting cluster information in the applicationdatabase (50).

[0145] Bots are independent components of the application that havespecific tasks to perform. In the case of temporal clustering bots,their primary task is to segment the component and sub-component ofvalue variables into distinct time regimes that share similarcharacteristics. The temporal clustering bot assigns a uniqueidentification (id) number to each “regime” it identifies and stores theunique id numbers in the cluster id table (157). Every time period withdata are assigned to one of the regimes. The cluster id for each regimeis saved in the data record for each item variable in the table where itresides. The item variables are segmented into a number of regimes lessthan or equal to the maximum specified by the user (20) in the systemsettings. The data are segmented using a competitive regressionalgorithm that identifies an overall, global model before splitting thedata and creating new models for the data in each partition. If theerror from the two models is greater than the error from the globalmodel, then there is only one regime in the data. Alternatively, if thetwo models produce lower error than the global model, then a third modelis created. If the error from three models is lower than from two modelsthen a fourth model is added. The process continues until adding a newmodel does not improve accuracy. Other temporal clustering algorithmsmay be used to the same effect. Every temporal clustering bot containsthe information shown in Table 21. TABLE 21 1. Unique ID number (basedon date, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5. Maximumnumber of clusters 6. Variable 1 . . . 6 + n. Variable n

[0146] When bots in block 304 have identified and stored regimeassignments for all time periods with data, processing advances to asoftware block 305.

[0147] The software in block 305 checks the bot date table (149) anddeactivates any variable clustering bots with creation dates before thecurrent system date. The software in block 305 then initializes bots inorder for each element of value. The bots: activate in accordance withthe frequency specified by the user (20) in the system settings table(140), retrieve the information from the system settings table (140),the metadata mapping table (141) and the element of value definitiontable (155) in order and define segments for the element of value databefore saving the resulting cluster information in the applicationdatabase (50).

[0148] Bots are independent components of the application that havespecific tasks to perform. In the case of variable clustering bots,their primary task is to segment the element of value variables intodistinct clusters that share similar characteristics. The clustering botassigns a unique id number to each “cluster” it identifies and storesthe unique id numbers in the cluster id table (157). Every item variablefor every element of value is assigned to one of the unique clusters.The cluster id for each variable is saved in the data record for eachitem variable in the table where it resides. The item variables aresegmented into a number of clusters less than or equal to the maximumspecified by the user (20) in the system settings table (140). The dataare segmented using the “default” clustering algorithm the user (20)specified in the system settings. The system of the present inventionprovides the user (20) with the choice of several clustering algorithmsincluding: an unsupervised “Kohonen” neural network, neural network,decision tree, support vector method, K-nearest neighbor, expectationmaximization (EM) and the segmental K-means algorithm. For algorithmsthat normally require the number of clusters to be specified, the botwill iterate the number of clusters until it finds the cleanestsegmentation for the data. Every variable clustering bot contains theinformation shown in Table 22. TABLE 22 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5. Element ofvalue 6. Clustering algorithm type 7. Maximum number of clusters 8.Variable 1 . . . to 8 + n. Variable n

[0149] When bots in block 305 have identified and stored clusterassignments for the item variables associated with each component andsubcomponent of value, processing advances to a software block 306.

[0150] The software in block 306 checks the bot date table (149) anddeactivates any predictive model bots with creation dates before thecurrent system date. The software in block 306 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the element of value definition table (155) and thecomponent of value definition table (156) required to initializepredictive model bots for each component of value.

[0151] Bots are independent components of the application that havespecific tasks to perform. In the case of predictive model bots, theirprimary task is to determine the relationship between the itemvariables, item performance indicators and composite variables(collectively hereinafter, “the variables”) and the components of value(and sub-components of value). Predictive model bots are initialized foreach component and sub-component of value. They are also initialized foreach cluster and regime of data in accordance with the cluster andregime assignments specified by the bots in blocks 304 and 305. A seriesof predictive model bots is initialized at this stage because it isimpossible to know in advance which predictive model type will producethe “best” predictive model for the data from each commercialenterprise. The series for each model includes 12 predictive model bottypes: neural network; CART; GARCH, projection pursuit regression;generalized additive model (GAM), redundant regression network;rough-set analysis, boosted Naïve Bayes Regression; MARS; linearregression; support vector method and stepwise regression. Additionalpredictive model types can be used to the same effect. The software inblock 306 generates this series of predictive model bots for the levelsof the enterprise shown in Table 23. TABLE 23 Predictive models byenterprise level Enterprise: Element variables relationship toenterprise revenue component of value Element variables relationship toenterprise expense subcomponents of value Element variables relationshipto enterprise capital change subcomponents of value Element of Value:Sub-element of value variables relationship to element of value

[0152] Every predictive model bot contains the information shown inTable 24. TABLE 24 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Component or subcomponent ofvalue 6. Global or Cluster (ID) and/or Regime (ID) 7. Element orSub-Element ID 8. Predictive Model Type 9. Variable 1 . . . to 9 + n.Variable n

[0153] After predictive model bots are initialized, the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, the bots retrieve the requireddata from the appropriate table in the application database (50) andrandomly partition the item variables, item performance indicators andcomposite variables into a training set and a test set. The software inblock 306 uses “bootstrapping” where the different training data setsare created by re-sampling with replacement from the original trainingset so data records may occur more than once. The same sets of data willbe used to train and then test each predictive model bot. When thepredictive model bots complete their training and testing, processingadvances to a block 307.

[0154] The software in block 307 determines if clustering improved theaccuracy of the predictive models generated by the bots in softwareblock 306. The software in block 307 uses a variable selection algorithmsuch as stepwise regression (other types of variable selectionalgorithms can be used) to combine the results from the predictive modelbot analyses for each type of analysis—with and without clustering—todetermine the best set of variables for each type of analysis. The typeof analysis having the smallest amount of error as measured by applyingthe mean squared error algorithm to the test data is given preference indetermining the best set of variables for use in later analysis. Thereare four possible outcomes from this analysis as shown in Table 25.TABLE 25 1. Best model has no clustering 2. Best model has temporalclustering, no variable clustering 3. Best model has variableclustering, no temporal clustering 4. Best model has temporal clusteringand variable clustering

[0155] If the software in block 307 determines that clustering improvesthe accuracy of the predictive models, then processing advances to asoftware block 310. Alternatively, if clustering doesn't improve theoverall accuracy of the predictive models, then processing advances to asoftware block 308.

[0156] The software in block 308 uses a variable selection algorithmsuch as stepwise regression (other types of variable selectionalgorithms can be used) to combine the results from the predictive modelbot analyses for each model to determine the best set of variables foreach model. The models having the smallest amount of error as measuredby applying the mean squared error algorithm to the test data is givenpreference in determining the best set of variables. As a result of thisprocessing, the best set of variables contain: the item variables, itemperformance indicators and composite variables that correlate moststrongly with changes in the components of value. The best set ofvariables will hereinafter be referred to as the “value drivers”.Eliminating low correlation factors from the initial configuration ofthe vector creation algorithms increases the efficiency of the nextstage of system processing. Other error algorithms alone or incombination may be substituted for the mean squared error algorithm.After the best set of variables have been selected and stored in theelement variables table (158) for all models at all levels, the softwarein block 308 tests the independence of the value drivers at theenterprise, element and sub-element level before processing advances toa block 309.

[0157] The software in block 309 checks the bot date table (149) anddeactivates any causal model bots with creation dates before the currentsystem date. The software in block 309 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141),the component of value definition table (156) and the element variablestable (158) in order to initialize causal model bots for eachenterprise, element of value and sub-element of value in accordance withthe frequency specified by the user (20) in the system settings table(140).

[0158] Bots are independent components of the application that havespecific tasks to perform. In the case of causal model bots, theirprimary task is to refine the item variable, item performance indicatorand composite variable selection to reflect only causal variables.(Note: these variables are grouped together to represent an elementvector when they are dependent). A series of causal model bots areinitialized at this stage because it is impossible to know in advancewhich causal model will produce the “best” vector for the best fitvariables from each model. The series for each model includes fivecausal model bot types: Tetrad, M M L, LaGrange, Bayesian and pathanalysis. The software in block 309 generates this series of causalmodel bots for each set of variables stored in the element variablestable (158) in the previous stage in processing. Every causal model botactivated in this block contains the information shown in Table 26.TABLE 26 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Component or subcomponent of value 6.Enterprise, Element or Sub-Element ID 7. Variable set 8. Causal modeltype

[0159] After the causal model bots are initialized by the software inblock 309, the bots activate in accordance with the frequency specifiedby the user (20) in the system settings table (140). Once activated,they retrieve the element variable information for each model from theelement variables table (158) and sub-divides the variables into twosets, one for training and one for testing. The same set of trainingdata is used by each of the different types of bots for each model.After the causal model bots complete their processing for each model,the software in block 309 uses a model selection algorithm to identifythe model that best fits the data for each enterprise, element orsub-element being analyzed. For the system of the present invention, across validation algorithm is used for model selection. The software inblock 309 saves the best fit causal factors in the vector table (159) inthe application database (50) and processing advances to a block 312.The software in block 312 tests the value drivers or vectors to see ifthere are “missing” value drivers that are influencing the results. Ifthe software in block 312 does not detect any missing value drivers,then system processing advances to a block 323. Alternatively, ifmissing value drivers are detected by the software in block 312, thenprocessing advances to a software block 321.

[0160] If software in block 307 determines that clustering improvespredictive model accuracy, then processing advances to block 310 asdescribed previously. The software in block 310 uses a variableselection algorithm such as stepwise regression (other types of variableselection algorithms can be used) to combine the results from thepredictive model bot analyses for each model and cluster to determinethe best set of variables for each model. The models having the smallestamount of error as measured by applying the mean squared error algorithmto the test data is given preference in determining the best set ofvariables. As a result of this processing, the best set of variablescontain: the item variables, item performance indicators and compositevariables that correlate most strongly with changes in the components ofvalue. The best set of variables will hereinafter be referred to as the“value drivers”. Eliminating low correlation factors from the initialconfiguration of the vector creation algorithms increases the efficiencyof the next stage of system processing. Other error algorithms alone orin combination may be substituted for the mean squared error algorithm.After the best set of variables have been selected and stored in theelement variables table (158) for all models at all levels, the softwarein block 310 tests the independence of the value drivers at theenterprise, element and sub-element level before processing advances toa block 311.

[0161] The software in block 311 checks the bot date table (149) anddeactivates any causal model bots with creation dates before the currentsystem date. The software in block 311 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141),the component of value definition table (156) and the element variablestable (158) in order to initialize causal model bots for eachenterprise, element of value and sub-element of value at every level inaccordance with the frequency specified by the user (20) in the systemsettings table (140).

[0162] Bots are independent components of the application that havespecific tasks to perform. In the case of causal model bots, theirprimary task is to refine the item variable, item performance indicatorand composite variable selection to reflect only causal variables.(Note: these variables are grouped together to represent a singleelement vector when they are dependent). In some cases it may bepossible to skip the correlation step before selecting causal the itemvariable, item performance indicator and composite variables. A seriesof causal model bots are initialized at this stage because it isimpossible to know in advance which causal model will produce the “best”vector for the best fit variables from each model. The series for eachmodel includes four causal model bot types: Tetrad, LaGrange, Bayesianand path analysis. The software in block 311 generates this series ofcausal model bots for each set of variables stored in the elementvariables table (158) in the previous stage in processing. Every causalmodel bot activated in this block contains the information shown inTable 27. TABLE 27 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Component or subcomponent ofvalue 6. Cluster (ID) and/or Regime (ID) 7. Enterprise, Element orSub-Element ID 8. Variable set 9. Causal model type

[0163] After the causal model bots are initialized by the software inblock 311, the bots activate in accordance with the frequency specifiedby the user (20) in the system settings table (140). Once activated,they retrieve the element variable information for each model from theelement variable table (158) and sub-divides the variables into twosets, one for training and one for testing. The same set of trainingdata is used by each of the different types of bots for each model.After the causal model bots complete their processing for each model,the software in block 311 uses a model selection algorithm to identifythe model that best fits the data for each enterprise, element orsub-element being analyzed. For the system of the present invention, across validation algorithm is used for model selection. The software inblock 311 saves the best fit causal factors in the vector table (159) inthe application database (50) and processing advances to block 312. Thesoftware in block 312 tests the value drivers or vectors to see if thereare “missing” value drivers that are influencing the results. If thesoftware in block 312 doesn't detect any missing value drivers, thensystem processing advances to a block 323. Alternatively, if missingvalue drivers are detected by the software in block 312, then processingadvances to a software block 321.

[0164] The software in block 321 prompts the user (20) via the variableidentification window (710) to adjust the specification(s) for theaffected enterprise, element of value or subelement of value. After theinput from the user (20) is saved in the system settings table (140)and/or the element of value definition table (155), system processingadvances to a software block 323. The software in block 323 checks thein the system settings table (140) and/or the element of valuedefinition table (155) to see if there any changes in structure. Ifthere have been changes in the structure, then processing advances to ablock 205 and the system processing described previously is repeated.Alternatively, if there are no changes in structure, then processingadvances to a block 325.

[0165] The software in block 325 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new one. If the calculation is new, then processing advances to asoftware block 326. Alternatively, if the calculation is not a newcalculation, then processing advances to a software block 333.

[0166] The software in block 326 checks the bot date table (149) anddeactivates any vector generation bots with creation dates before thecurrent system date. The software in block 326 then initializes bots foreach element and sub-element of value for the enterprise. The botsactivate in accordance with the frequency specified by the user (20) inthe system settings table (140), retrieve the information from thesystem settings table (140), the metadata mapping table (141), thecomponent of value definition table (156) and the element variablestable (158) in order to initialize vector generation bots for eachenterprise, element of value and sub-element of value in accordance withthe frequency specified by the user (20) in the system settings table(140).

[0167] Bots are independent components of the application that havespecific tasks to perform. In the case of vector generation bots, theirprimary task is to produce formulas, (hereinafter, vectors) thatsummarize the relationship between the item variables, item performanceindicators and composite variables for the element or sub-element andchanges in the component or sub-component of value being examined.(Note: these variables are simply grouped together to represent anelement vector when they are dependent). A series of vector generationbots are initialized at this stage because it is impossible to know inadvance which vector generation algorithm will produce the “best” vectorfor the best fit variables from each model. The series for each modelincludes four vector generation bot types: data fusion, polynomial,induction and LaGrange. Other vector generation algorithms can be usedto the same effect. The software in block 326 generates this series ofvector generation bots for each set of variables stored in the elementvariables table (158). Every vector generation bot contains theinformation shown in Table 28. TABLE 28 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5. Maximumnumber of regimes 6. Enterprise or Industry 7. Factor 1 . . . to 7 + n.Factor n

[0168] When bots in block 326 have identified and stored vectors for alltime periods with data, processing advances to a software block 327.

[0169] The software in block 327 checks the bot date table (149) anddeactivates any temporal clustering bots with creation dates before thecurrent system date. The software in block 327 then initializes bots formarket value factors for each enterprise with a market price and for theindustry. The bots activate in accordance with the frequency specifiedby the user (20) in the system settings table (140), retrieve theinformation from the system settings table (140), the metadata mappingtable (141) and the sentiment factors table (169) in order and defineregimes for the market value factor data before saving the resultingregime information in the application database (50).

[0170] Bots are independent components of the application that havespecific tasks to perform. In the case of temporal clustering bots formarket value factors, their primary tasks are to identify the bestmarket value indicator (price, relative price, yield or first derivativeof price change) to use for market factor analysis and then to segmentthe market value factors into distinct time regimes that share similarcharacteristics. The temporal clustering bots select the best valueindicator by grouping the universe of stocks using each of the fourvalue indicators and then comparing the clusters to the known groupingsof the S&P 500. The temporal clustering bots then use the identifiedvalue indicator in the analysis of temporal clustering. The bots assigna unique id number to each “regime” it identifies and stores the uniqueid numbers in the cluster id table (157). Every time period with data isassigned to one of the regimes,. The cluster id for each regime is alsosaved in the data record for each market value factor in the table whereit resides. The market value factors are segmented into a number ofregimes less than or equal to the maximum specified by the user (20) inthe system settings table (140). The factors are segmented using acompetitive regression algorithm that identifies an overall, globalmodel before splitting the data and creating new models for the data ineach partition. If the error from the two models is greater than theerror from the global model, then there is only one regime in the data.Alternatively, if the two models produce lower error than the globalmodel, then a third model is created. If the error from three models islower than from two models then a fourth model is added. The processcontinues until adding a new model does not improve accuracy. Othertemporal clustering algorithms may be used to the same effect. Everytemporal clustering bot contains the information shown in Table 29.TABLE 29 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Maximum number of regimes 6.Enterprise or Industry 7. Value indicator (price, relative price, yield,derivative, etc.) 8. Factor 1 . . . to 8 + n. Factor n

[0171] When bots in block 327 have identified and stored regimeassignments for all time periods with data, processing advances to asoftware block 328.

[0172] The software in block 328 checks the bot date table (149) anddeactivates any causal factor bots with creation dates before thecurrent system date. The software in block 328 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the element of value definition table (155) and thesentiment factors table (169) in order to initialize causal market valuefactor bots for the enterprise and for the industry in accordance withthe frequency specified by the user (20) in the system settings table(140).

[0173] Bots are independent components of the application that havespecific tasks to perform. In the case of causal factor bots, theirprimary task is to identify the item variables, item performanceindicators, composite variables and market value factors that are causalfactors for stock price movement. (Note: these variables are groupedtogether when they are dependent). For each enterprise and industry thecausal factors are those that drive changes in the value indicatoridentified by the temporal clustering bots. A series of causal factorbots are initialized at this stage because it is impossible to know inadvance which causal factors will produce the “best” model for eachenterprise and industry. The series for each model includes five causalmodel bot types: Tetrad, LaGrange, M M L, Bayesian and path analysis.Other causal models can be used to the same effect. The software inblock 328 generates this series of causal model bots for each set ofvariables stored in the element variables table (158) in the previousstage in processing. Every causal factor bot activated in this blockcontains the information shown in Table 30. TABLE 30 1. Unique ID number(based on date, hour, minute, second of creation) 2. Creation date(date, hour, minute, second) 3. Mapping information 4. Storage location6. Enterprise or Industry 7. Regime 8. Value indicator (price, relativeprice, yield, derivative, etc.) 9. Causal model type

[0174] After the causal factor bots are initialized by the software inblock 328, the bots activate in accordance with the frequency specifiedby the user (20) in the system settings table (140). Once activated,they retrieve the required information from the element of valuedefinition table (155) and the sentiment factors table (169) andsub-divide the data into two sets, one for training and one for testing.The same set of training data is used by each of the different types ofbots for each model. After the causal factor bots complete theirprocessing for the enterprise and/or industry, the software in block 328uses a model selection algorithm to identify the model that best fitsthe data for each enterprise or industry. For the system of the presentinvention, a cross validation algorithm is used for model selection. Thesoftware in block 328 saves the best fit causal factors in the sentimentfactors table (169) in the application database (50) and processingadvances to a block 329. The software in block 329 tests to see if thereare “missing” causal market value factors that are influencing theresults. If the software in block 329 does not detect any missing marketvalue factors, then system processing advances to a block 330.Alternatively, if missing market value factors are detected by thesoftware in block 329, then processing returns to software block 321 andthe processing described in the preceding section is repeated.

[0175] The software in block 330 checks the bot date table (149) anddeactivates any industry rank bots with creation dates before thecurrent system date. The software in block 330 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the vector table (159) and the sentiment factors table(169) in order to initialize industry rank bots for the enterprise if ithas a public stock market price and for the industry in accordance withthe frequency specified by the user (20) in the system settings table(140).

[0176] Bots are independent components of the application that havespecific tasks to perform. In the case of industry rank bots, theirprimary task is to determine the relative position of the enterprisebeing evaluated on the causal attributes identified in the previousprocessing step. (Note: these variables are grouped together when theyare dependent). The industry rank bots use Data Envelopement Analysis(hereinafter, DEA) to determine the relative industry ranking of theenterprise being examined. The software in block 330 generates industryrank bots for the enterprise being evaluated. Every industry rank botactivated in this block contains the information shown in Table 31.TABLE 31 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Enterprise

[0177] After the industry rank bots are, initialized by the software inblock 330, the bots activate in accordance with the frequency specifiedby the user (20) in the system settings table (140). Once activated,they retrieve the item variables, item performance indicators, compositevariables and market value factors for the enterprise from theapplication database (50) and sub-divides the factors into two sets, onefor training and one for testing. After the industry rank bots completetheir processing for the enterprise the software in block 330 saves theindustry ranks in the vector table (159) in the application database(50) and processing advances to a block 331.

[0178] The software in block 331 checks the bot date table (149) anddeactivates any option bots with creation dates before the currentsystem date. The software in block 331 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141),the basic financial system database (143), the external database table(146) and the advanced finance system table (147) in order to initializeoption bots for the industry and the enterprise.

[0179] Bots are independent components of the application that havespecific tasks to perform. In the case of option bots, their primarytasks are to calculate the discount rate to be used for valuing the realoptions and to value the real options for the industry and theenterprise. The discount rate for enterprise real options is calculatedby adding risk factors for each causal soft asset to a base discountrate. The risk factor for each causal soft asset is determined by a twostep process. The first step in the process divides the maximum realoption discount rate (specified by the user in system settings) by thenumber of causal soft assets. The second step in the process determinesif the enterprise is highly rated on the causal soft assets anddetermines an appropriate risk factor. If the enterprise is highlyranked on the soft asset, then the discount rate is increased by arelatively small amount for that causal soft asset. Alternatively, ifthe enterprise has a low ranking on a causal soft asset, then thediscount rate is increased by a relatively large amount for that causalsoft asset as shown below in Table 32. TABLE 32 Maximum discount rate =50%, Causal soft assets = 5 Maximum risk factor/soft asset = 50%/5 = 10%Industry Rank on Soft Asset % of Maximum 1  0% 2 25% 3 50% 4 75% 5 orhigher 100%  Causal Soft Asset: Relative Rank Risk Factor Brand 1  0%Channel 3  5% Manufacturing Process 4 7.5%  Strategic Alliances 5 10%Vendors 2 2.5%  Subtotal 25% Base Rate 12% Discount Rate 37%

[0180] The discount rate for industry options is calculated using aweighted average total cost of capital approach in a manner that is wellknown. The base discount rate for enterprise options is calculated usinga total average cost of capital (TACC) approach shown below.

TACC=cost of debt×(debt value/total value)+cost of equity×(equityvalue/total value)+cost of insurance×(insurance value/total value)

[0181] After the appropriate discount rates are determined, the value ofeach real option is calculated using Black Scholes algorithms in amanner that is well known. The real option can be valued using otheralgorithms including binomial, neural network or dynamic programmingalgorithms. The software in block 331 values option bots for the industyand the enterprise. Industry option bots utilize the industry cost ofcapital for all calculations.

[0182] Option bots contain the information shown in Table 33. TABLE33 1. Unique ID number (based on date, hour, minute, second of creation)2. Creation date (date, hour, minute, second) 3. Mapping information 4.Storage location 5. Industry or Enterprise ID 6. Real option type(Industry or Enterprise) 7. Real option 8. Allocation percentage (ifapplicable)

[0183] After the option bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated, the bots retrieveinformation for the industry and the enterprise from the basic financialsystem database (143), the external database table (146) and theadvanced finance system table (147) in order to complete the optionvaluation. After the discount has been determined the value of the realoption is calculated using Black Schole's algorithms in a manner that iswell known. The resulting values are then saved in the real option valuetable (162) in the application database (50) before processing advancesto a block 332.

[0184] The software in block 332 uses the results of the DEA analysis inthe prior processing block and the percentage of industry real optionscontrolled by the enterprise to determine the allocation percentage forindustry options. The more dominant the enterprise, as indicated by theindustry rank for the intangible element indicators, the greater theallocation of industry real options. When the allocation of options hasbeen determined and the resulting values stored in the real option valuetable (162) in the application database (50), processing advances to ablock 333.

[0185] The software in block 333 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation, a value analysis or a structure change, then processingadvances to a software block 341. Alternatively, if the calculation isnew a value analysis or a structure change, then processing advances toa software block 343.

[0186] The software in block 341 checks the bot date table (149) anddeactivates any cash flow bots with creation dates before the currentsystem date. The software in the block then retrieves the informationfrom the system settings table (140), the metadata mapping table (141)and the component of value definition table (156) in order to initializecash flow bots for the enterprise in accordance with the frequencyspecified by the user (20) in the system settings table (140).

[0187] Bots are independent components of the application that havespecific tasks to perform. In the case of cash flow bots, their primarytasks are to calculate the cash flow for the enterprise for every timeperiod where data are available and to forecast a steady state cash flowfor the enterprise. Cash flow is calculated using a well known formulawhere cash flow equals period revenue minus period expense plus theperiod change in capital plus non-cash depreciation/amortization for theperiod. The steady state cash flow is calculated for the enterpriseusing forecasting methods identical to those disclosed previously inU.S. Pat. 5,615,109 to forecast revenue, expenses, capital changes anddepreciation separately before calculating the cash flow. The softwarein block 334 initializes cash flow bots for the enterprise.

[0188] Every cash flow bot contains the information shown in Table 34.TABLE 34 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Enterprise ID 6. Components of value

[0189] After the cash flow bots are initialized, the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated the bots retrieve thecomponent of value information for the enterprise from the component ofvalue definition table (156). The cash flow bots then complete thecalculation and forecast of cash flow for the enterprise before savingthe resulting values by period in the cash flow table (161) in theapplication database (50) before processing advances to a block 342.

[0190] The software in block 342 checks the bot date table (149) anddeactivates any element life bots with creation dates before the currentsystem date. The software in block 341 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141)and the element of value definition table (155) in order to initializeelement life bots for each element and sub-element of value in theenterprise being examined.

[0191] Bots are independent components of the application that havespecific tasks to perform. In the case of element life bots, theirprimary task is to determine the expected life of each element andsub-element of value. There are three methods for evaluating theexpected life of the elements and sub-elements of value. Elements ofvalue that are defined by a population of members or items (such as:channel partners, customers, employees and vendors) will have theirlives estimated by analyzing and forecasting the lives of the members ofthe population. The forecasting of member lives will be determined bythe “best” fit solution from competing life estimation methods includingthe Iowa type survivor curves, Weibull distribution survivor curves,Gompertz-Makeham survivor curves, polynomial equations and theforecasting methodology disclosed in U.S. Pat. No. 5,615,109. Elementsof value (such as some parts of Intellectual Property i.e. patents) thathave legally defined lives will have their lives calculated using thetime period between the current date and the expiration date of theelement or sub-element. Finally, elements of value and sub-element ofvalue (such as brand names, information technology and processes) thatmay not have defined lives and that may not consist of a collection ofmembers will have their lives estimated by comparing the relativestrength and stability of the element vectors with the relativestability of the enterprise Competitive Advantage Period (CAP) estimate.The resulting values are stored in the element of value definition table(155) for each element and sub-element of value.

[0192] Every element life bot contains the information shown in Table35. TABLE 35 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Element or Sub-Element of value 6.Life estimation method (item analysis, date calculation or relative CAP)

[0193] After the element life bots are initialized, they are activatedin accordance with the frequency specified by the user (20) in thesystem settings table (140). After being activated, the bots retrieveinformation for each element and sub-element of value from the elementof value definition table (155) in order to complete the estimate ofelement life. The resulting values are then saved in the element ofvalue definition table (155) in the application database (50) beforeprocessing advances to a block 343.

[0194] The software in block 343 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation, a value analysis or a structure change. If thecalculation is not a new calculation or a structure change, thenprocessing advances to a software block 402. Alternatively, if thecalculation is new or a structure change, then processing advances to asoftware block 345.

[0195] The software in block 345 checks the bot date table (149) anddeactivates any component capitalization bots with creation dates beforethe current system date. The software in block 341 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141) and the component of value definition table (156) in orderto initialize component capitalization bots for the enterprise.

[0196] Bots are independent components of the application that havespecific tasks to perform. In the case of component capitalization bots,their task is to determine the capitalized value of the components andsubcomponents of value, forecast revenue, expense or capitalrequirements for the enterprise in accordance with the formula shown inTable 36. TABLE 36 Value = F_(f1)/(1 + K) + F_(f2)/(1 + K)² +F_(f3)/(1 + K)³ + F_(f4)/(1 + K)⁴ + (F_(f4) × (1 + g))/(1 + K)⁵) +(F_(f4) × (1 + g)²)/(1 + K)⁶) . . . + (F_(f4) × (1 + g)^(N))/(1 +K)^(N+4)) Where: F_(fx) = Forecast revenue, expense or capitalrequirements for year x after valuation date (from advanced financesystem) N = Number of years in CAP (from prior calculation) K = Totalaverage cost of capital - % per year (from prior calculation) g =Forecast growth rate during CAP - % per year (from advanced financialsystem)

[0197] After the capitalized value of every component and sub-componentof value is complete, the results are stored in the component of valuedefinition table (156) in the application database (50).

[0198] Every component capitalization bot contains the information shownin Table 37. TABLE 37 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Enterprise ID 6. Component ofvalue (revenue, expense or capital change) 7. Sub component of value

[0199] After the component capitalization bots are initialized theyactivate in accordance with the frequency specified by the user (20) inthe system settings table (140). After being activated, the botsretrieve information for each component and sub-component of value fromthe advanced finance system table (147) and the component of valuedefinition table (156) in order to calculate the capitalized value ofeach component. The resulting values are then saved in the component ofvalue definition table (156) in the application database (50) beforeprocessing advances to a block 347.

[0200] The software in block 347 checks the bot date table (149) anddeactivates any element valuation bots with creation dates before thecurrent system date. The software in block 347 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the element of value definition table (155), the componentof value definition table (156) in order to initialize valuation botsfor each element and sub-element of value.

[0201] Bots are independent components of the application that havespecific tasks to perform. In the case of element valuation bots, theirtask is to calculate the contribution of every element of value andsub-element of value in the enterprise using the overall procedureoutlined in Table 5. The first step in completing the calculation inaccordance with the procedure outlined in Table 5, is determining therelative contribution of element and sub-element of value by using aseries of predictive models to find the best fit relationship between:

[0202] 1. The element of value vectors and the enterprise components ofvalue; and

[0203] 2. The sub-element of value vectors and the element of value theycorrespond to.

[0204] The system of the present invention uses 12 different types ofpredictive models to determine relative contribution: neural network;CART; projection pursuit regression; generalized additive model (GAM);GARCH; MMDR; redundant regression network; boosted Naïve BayesRegression; the support vector method; MARS; linear regression; andstepwise regression to determine relative contribution. The model havingthe smallest amount of error as measured by applying the mean squarederror algorithm to the test data is the best fit model. The “relativecontribution algorithm” used for completing the analysis varies with themodel that was selected as the “best-fit”. For example, if the“best-fit” model is a neural net model, then the portion of revenueattributable to each input vector is determined by the formula shown inTable 38. TABLE 38$\left( {\sum\limits_{k = 1}^{k = m}{\sum\limits_{j = 1}^{j = n}{I_{j\quad k} \times {O_{k}/{\sum\limits_{j = 1}^{j = n}I_{ik}}}}}} \right)/{\sum\limits_{k = 1}^{k = m}{\sum\limits_{j = 1}^{j = n}{I_{j\quad k} \times O_{k}}}}$

[0205] After the relative contribution of each enterprise, element ofvalue and sub-element of value is determined, the results of thisanalysis are combined with the previously calculated informationregarding element life and capitalized component value to complete thevaluation of each: enterprise contribution, element of value andsub-element using the approach shown in Table 39. TABLE 39 Element GrossValue Percentage Life/CAP Net Value Revenue value = $120 M 20% 80% Value= $19.2 M Expense value = ($80 M) 10% 80% Value = ($6.4) M Capital value= ($5 M)  5% 80% Value = ($0.2) M Total value = $35 M Net value for thiselement: Value = $12.6 M

[0206] The resulting values are stored in the element of valuedefinition table (155) for each element and sub-element of value of theenterprise.

[0207] Every valuation bot contains the information shown in Table 40.TABLE 40 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Element of value or sub-element ofvalue 6. Element of value ID

[0208] After the valuation bots are initialized by the software in block347 they activate in accordance with the frequency specified by the user(20) in the system settings table (140). After being activated, the botsretrieve information from the element of value definition table (155)and the component of value definition table (156) in order to completethe valuation. The resulting values are then saved in the element ofvalue definition table (155) in the application database (50) beforeprocessing advances to a block 351.

[0209] The software in block 351 checks the bot date table (149) anddeactivates any residual bots with creation dates before the currentsystem date. The software in block 351 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141)and the element of value definition table (155) in order to initializeresidual bots for the enterprise.

[0210] Bots are independent components of the application that havespecific tasks to perform. In the case of residual bots, their task isto retrieve data in order from the element of value definition table(155) and the component of value definition table (156) and thencalculate the residual going concern value for the enterprise inaccordance with the formula shown in Table 41. TABLE 41 Residual GoingConcern Value = Total Current-Operation Value − Σ Financial Asset Values− Σ Elements of Value

[0211] Every residual bot contains the information shown in Table 42.TABLE 42 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Enterprise ID

[0212] After the residual bots are initialized they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated, the bots retrieveinformation from the element of value definition table (155) and thecomponent of value definition table (156) in order to complete theresidual calculation for the enterprise. After the calculation iscomplete, the resulting values are then saved in the element of valuedefinition table (155) in the application database (50) beforeprocessing advances to a block 352.

[0213] The software in block 352 checks the bot date table (149) anddeactivates any sentiment calculation bots with creation dates beforethe current system date. The software in block 352 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the external database table (146), the element of valuedefinition table (155), the component of value definition table (156)and the real option value table (162) in order to initialize sentimentcalculation bots for the enterprise.

[0214] Bots are independent components of the application that havespecific tasks to perform. In the case of sentiment calculation bots,their task is to retrieve data in order from: the external databasetable (146), the element of value definition table (155), the componentof value definition table (156) and the real option value table (162)and then calculate the sentiment for the enterprise in accordance withthe formula shown in Table 43. TABLE 43 Sentiment = Total Market Value −Total Current-Operation Value − Σ Real Option Values

[0215] Every sentiment calculation bot contains the information shown inTable 44. TABLE 44 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Enterprise ID

[0216] After the sentiment calculation bots are initialized theyactivate in accordance with the frequency specified by the user (20) inthe system settings table (140). After being activated, the botsretrieve information from the external database table (146), the elementof value definition table (155), the component of value definition table(156) and the real option value table (162) in order to complete thesentiment calculation for each enterprise. After the calculation iscomplete, the resulting values are then saved in the enterprisesentiment table (166) in the application database (50) before processingadvances to a block 353.

[0217] The software in block 353 checks the bot date table (149) anddeactivates any sentiment analysis bots with creation dates before thecurrent system date. The software in block 353 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the external database table (146), the element of valuedefinition table (155), the component of value definition table (156),the real option value table (162), the enterprise sentiment table (166)and the sentiment factors table (169) in order to initialize sentimentanalysis bots for the enterprise.

[0218] Bots are independent components of the application that havespecific tasks to perform. In the case of sentiment analysis bots, theirprimary task is to determine the composition of the calculated sentimentby comparing the portion of overall market return that related to thedifferent elements of value and the calculated valuation for eachelement of value as shown below in Table 45. TABLE 45 Total EnterpriseMarket Value = $100 Billion, 10% related to Brand factors Implied BrandValue = $100 Billion × 10% = $10 Billion Valuation of Brand Element ofValue = $6 Billion Increase/(Decrease) in Enterprise Real Option ValuesDue to Brand = $1.5 Billion Industry Option Allocation Due to Brand =$1.0 Billion Brand Sentiment = $10 − $6 − $1.5 − $1.0 = $1.5 Billion

[0219] Every sentiment analysis bot contains the information shown inTable 46. TABLE 46 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Enterprise ID

[0220] After the sentiment analysis bots are initialized, they activatein accordance with the frequency specified by the user (20) in thesystem settings table (140). After being activated, the bots retrieveinformation from the system settings table (140), the metadata mappingtable (141), the enterprise sentiment table (166) and the sentimentfactors table (169) in order to analyze sentiment. The resultingbreakdown of sentiment is then saved in the enterprise sentiment table(169) in the application database (50) before processing advances to ablock 402.

Risk Reduction Bots

[0221] The flow diagram in FIG. 7 details the processing that iscompleted by the portion of the application software (400) that analyzesand develops a risk reduction strategy for the commercial enterpriseusing the system.

[0222] System processing in this portion of the application software(400) begins in a block 402. The software in block 402 checks the systemsettings table (140) in the application database (50) to determine ifthe current calculation is a new calculation or a structure change. Ifthe calculation is not a new calculation or a structure change, thenprocessing advances to a software block 412. Alternatively, if thecalculation is new or a structure change, then processing advances to asoftware block 403.

[0223] The software in block 403 checks the bot date table (149) anddeactivates any statistical bots with creation dates before the currentsystem date. The software in block 403 then retrieves the informationfrom the system settings table (140), the external database table (146),the element of value definition table (155), the element variables table(158) and the sentiment factor table (169) in order to initializestatistical bots for each causal value driver and market value factor.

[0224] Bots are independent components of the application that havespecific tasks to perform. In the case of statistical bots, theirprimary tasks are to calculate and store statistics such as mean,median, standard deviation, slope, average period change, maximum periodchange, variance and covariance for each causal value driver and marketvalue factor for every regime. Covariance with the market as a whole isalso calculated for each value driver and market value factor. Everystatistical bot contains the information shown in Table 47. TABLE 47 1.Unique ID number (based on date, hour, minute, second of creation) 2.Creation date (date, hour, minute, second) 3. Mapping information 4.Storage location 5. Regime 6. Value Driver or Market Value Factor

[0225] When bots in block 403 have identified and stored statistics foreach causal value driver and market value factor in the statistics table(170), processing advances to a software block 404.

[0226] The software in block 404 checks the bot date table (149) anddeactivates any risk reduction activity bots with creation dates beforethe current system date. The software in block 404 then retrieves theinformation from the system settings table (140), the external databasetable (146), the element of value definition table (155), the elementvariables table (158), the sentiment factor table (169) and thestatistics table (170) in order to initialize risk reduction activitybots for each causal value driver and market value factor.

[0227] Bots are independent components of the application that havespecific tasks to perform. In the case of risk reduction activity bots,their primary tasks are to identify actions that can be taken by theenterprise to reduce risk. For example, if one customer presents asignificant risk to the enterprise, then the risk reduction bot mightidentify a reduction in the credit line for that customer to reduce therisk. Every risk reduction activity bot contains the information shownin Table 48. TABLE 48 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Value driver or Market valuefactor

[0228] When bots in block 404 have identified and stored risk reductionactivities in the risk reduction activity table (179), processingadvances to a software block 405.

[0229] The software in block 405 checks the bot date table (149) anddeactivates any extreme value bots with creation dates before thecurrent system date. The software in block 405 then retrieves theinformation from the system settings table (140), the external databasetable (146), the element of value definition table (155), the elementvariables table (158) and the sentiment factor table (169) in order toinitialize extreme value bots in accordance with the frequency specifiedby the user (20) in the system settings table (140).

[0230] Bots are independent components of the application that havespecific tasks to perform. In the case of extreme value bots, theirprimary task is to identify the extreme values for each causal valuedriver and market value factor. The extreme value bots use the Blocksmethod and the peak over threshold method to identify extreme values.Other extreme value algorithms can be used to the same effect. Everyextreme value bot activated in this block contains the information shownin Table 49. TABLE 49 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Method 6. Value driver orMarket value factor

[0231] After the extreme value bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation from the system settings table (140), the external databasetable (146), the element of value definition table (155), the elementvariables table (158) and the sentiment factor table (169) and determinethe extreme value range for each value driver or market value factor.The bot saves the extreme values for each causal value driver and marketvalue factor in the statistics table (170) in the application database(50) and processing advances to a block 409.

[0232] The software in block 409 checks the bot date table (149) anddeactivates any scenario bots with creation dates before the currentsystem date. The software in block 409 then retrieves the informationfrom the system settings table (140), the operation system table (144),the external database table (146), the advanced finance system table(147), the element of value definition table (155), the sentimentfactors table (169) and the statistics table (170) in order toinitialize scenario bots in accordance with the frequency specified bythe user (20) in the system settings table (140).

[0233] Bots are independent components of the application that havespecific tasks to perform. In the case of scenario bots, their primarytask is to identify likely scenarios for the evolution of the causalvalue drivers and market value factors. The scenario bots useinformation from the advanced finance system and external databases toobtain forecasts for individual causal factors before using thecovariance information stored in the statistics table (170) to developforecasts for the other causal value drivers and factors under normalconditions. They also use the extreme value information calculated bythe previous bots and stored in the statistics table (170) to calculateextreme scenarios. Every scenario bot activated in this block containsthe information shown in Table 50. TABLE 50 1. Unique ID number (basedon date, hour, minute, second of creation) 2. Creation date (date, hour,minute, second) 3. Mapping information 4. Storage location 5. Type:Normal or Extreme 5. Enterprise

[0234] After the scenario bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation and develop a variety of scenarios as described previously.After the scenario bots complete their calculations they save theresulting scenarios in the scenarios table (171) in the applicationdatabase (50) and processing advances to a block 410.

[0235] The software in block 410 checks the bot date table (149) anddeactivates any simulation bots with creation dates before the currentsystem date. The software in block 410 then retrieves the informationfrom the system settings table (140), the operation system table (144),the advanced finance system table (147), the element of value definitiontable (155), the external database table (156), the sentiment factorstable (169), the statistics table (170), the scenarios table (171) andthe generic risk table (178) in order to initialize simulation bots inaccordance with the frequency specified by the user (20) in the systemsettings table (140).

[0236] Bots are independent components of the application that havespecific tasks to perform. In the case of simulation bots, their primarytask is to run three different types of simulations for the enterprise.The simulation bots run simulations of organizational financialperformance and valuation using: the two types of scenarios generated bythe scenario bots—normal and extreme, they also run an unconstrainedgenetic algorithm simulation that evolves to the most negative scenario.In addition to examining the economic factors that were identified inthe previous analysis, the bots simulate the impact of generic riskslike fire, earthquakes, floods and other weather-related pheonomenalthat are un-correlated with the economic scenarios. Every simulation botactivated in this block contains the information shown in Table 51.TABLE 51 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Type: Normal, Extreme or GeneticAlgorithm 6. Enterprise

[0237] After the simulation bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation and simulate the financial performance and value impact ofthe different scenarios. After the simulation bots complete theircalculations, the resulting forecasts are saved in the simulations table(168) and the summary xml table (177) in the application database (50)and processing advances to a block 411.

[0238] The software in block 411 continually runs an analysis to definethe optimal risk reduction strategy for each of the identified normaland extreme scenarios. It does this by first retrieving from the systemsettings table (140), the operation system table (144), the externaldatabase table (146), the advanced finance system table (147), theelement of value definition table (155), the sentiment factors table(169), the statistics table (170), the scenario table (171), the riskreduction products table (173) and the risk reduction activity table(179) which is the information required to initialize the optimizationalgorithm. The software in the block determines the optimal mix of riskreduction products (derivative purchase, insurance purchase, etc.) andrisk reduction activities (reducing credit limits for certain customers,shifting production from high risk to lower risk countries, etc.) forthe company under each scenario given the confidence intervalestablished by the user (20) in the system settings using a linearprogramming optimization algorithm. A multi criteria optimizationdetermines the best mix for reducing risk under both normal and extremescenarios. Other optimization algorithms can be used at this point andall optimizations consider the effect of changes in the cost of capitalon the optimal solution. In any event, the resulting product andactivity mix for each set of scenarios and the combined analysis issaved in the optimal mix table (175) and the xml summary table (177) inthe application database (50) and the revised simulations are saved inthe simulations table (168) before processing passes to a software block412. The shadow prices from these optimizations are also stored in therisk reduction products table (173) and the xml summary table (177) foruse in identifying new risk reduction products that the company may wishto purchase and/or new risk reduction activities the company may wish todevelop.

[0239] The software in block 412 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new, calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 502. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 413.

[0240] The software in block 413 checks the bot date table (149) anddeactivates any impact bots with creation dates before the currentsystem date. The software in block 413 then retrieves the informationfrom the system settings table (140), the operation system table (144),the external database table (146), the advanced finance system table(147), the element of value definition table (155), the simulationstable (168), the sentiment factors table (169), the statistics table(170), the scenario table (171) and the optimal mix table (175) in orderto initialize value impact bots in accordance with the frequencyspecified by the user (20) in the system settings table (140).

[0241] Bots are independent components of the application that havespecific tasks to perform. In the case of impact bots, their primarytask is to determine the value impact of each risk reduction product andactivity—those included in the optimal mix and those that aren't—on thedifferent scenarios. Every impact bot contains the information shown inTable 52. TABLE 52 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (date, hour, minute, second) 3.Mapping information 4. Storage location 5. Enterprise 6. Risk reductionproduct or activity

[0242] After the value impact bots are initialized by the software inblock 413, they activate in accordance with the frequency specified bythe user (20) in the system settings table (140). After being activated,the bots retrieve information in order to revise the simulations ofenterprise performance and determine the risk reduction impact of eachproduct on each simulation. The resulting forecast of value impacts arethen saved in the risk reduction products table (173) or the riskreduction activity table (179) as appropriate in the applicationdatabase (50) before processing advances to a block 414.

[0243] The software in block 414 prepares and displays a listing fromhighest impact to lowest impact for each risk reduction product underthe normal scenarios, the extreme scenarios and the combined(multi-criteria) analysis using the prioritized listing display window(706). The optimal mix for the normal and extreme scenarios aredetermined by calculating the weighted average sum of the differentscenarios where the weighting is determined by the relative likelihoodof the scenario. The display identifies the optimal mix from thecombined analysis as the recommended solution for enterprise riskreduction. At this point, the user (20) is given the option of:

[0244] 1. Editing (adding or deleting products and activities) from therecommended solution;

[0245] 2. Selecting the optimal mix from the normal scenario;

[0246] 3. Selecting and then editing the optimal mix from the normalscenarios;

[0247] 4. Selecting the optimal mix from the extreme scenario;

[0248] 5. Selecting and then editing the optimal mix from the extremescenarios; or

[0249] 6. Leaving the default choice in place.

[0250] After the user (20) has finished the review and the optional editof the selected mix, any changes are saved in the optimal mix table(175) in the application database (50) and processing advances to asoftware block 502. It should be noted that the processing of the riskreduction bot segment can, with very minor changes, also be used toanalyze the impact of value enhancing changes on the enterprise. Thiscould include a value maximization analysis and/or a multi-criteriamaximum value, minimum risk optimization.

Output

[0251] The flow diagram in FIG. 8 details the processing that iscompleted by the portion of the application software (500) thatgenerates a summary of the risk, liquidity and foreign exchange positionof the company, places orders to purchase the optimal mix of riskreduction products and optionally prints management reports. Processingin this portion of the application starts in software block 502.

[0252] The software in block 502 checks the optimal mix table (175) inthe application database (50) to determine which risk reductionactivities have been included in the optimal mix. If risk reductionactivities have been included in the optimal mix, then the software inthis block prepares summaries of the changes and transmits them to theaffected financial, operational and/or soft asset management system(s).For example, if the option to reduce the credit line for a certaincustomer has been accepted, then the customer relationship managementsystem and the accounts receivable system will be updated with the newcredit limit information by a transmission from the software in thisblock. Alternatively, if there are no risk reduction activities in theoptimal mix, then processing advances directly to a software block 503.

[0253] The software in block 503 retrieves information from the systemsettings table (140) and the advanced finance system table (147) that isrequired to calculate the minimum amount of cash that will be availablefor investment in risk reduction during the next 36 month period. Thesystem settings table (140) contains the minimum amount of cash andavailable securities that the user (20) indicated win order forenterprise operation while the advanced finance system table (147)contains a forecast of the cash balance for the enterprise for eachperiod during the next 36 months. A summary of the available cash andcash deficits by currency, by month, by enterprise for the next 36months is stored in a summary xml format in the xml summary table (177)during this stage of processing. After the amount of available cash foreach enterprise is calculated and stored in the risk reduction purchasetable (165), processing advances to a software block 504.

[0254] The software in block 504 assembles the previously developedsummaries of cash position, foreign exchange requirements, risks,scenarios and statistics into a xml summary profile of the enterprise.This summary profile is transferred via the network (45) to an exchangeor other risk transfer provider (600).

[0255] The software in block 514 analyzes the mix of risk reductionproducts and swaps recommended by an exchange or other risk transferprovider (600) to determine the percentage reduction in financialperformance volatility that their purchase will produce for theenterprise. If the previously completed sentiment analysis indicatedthat financial performance volatility was a driver of market value, thenthe software in block 514 will retrieve the required information fromthe sentiment factors table (169) and estimate the value increase thatwill be produced by the decreased volatility. The software in block 514also confirms that the products and/or swaps recommended by the exchangeor other risk transfer provider (600) can be purchased using availablecash for a total expenditure, counting both prior purchases and plannedpurchases, that is less than or equal to the maximum investment amountestablished by the user (20) in system settings table (140). If theplanned purchases are within the guidelines established by the user(20), then the software generates a purchase order for the additionalrisk reduction products and/or swaps. Alternatively, if there isn'tavailable cash or if the planned purchase exceeds the expenditureguideline established by the user (20), then a message indicating theproblem(s) is prepared. In any event, the software in block 514 displaysthe resulting message or purchase order to the user (20) via thepurchase review data window (711). The purchase review data window (711)also displays the estimate of value increase, if any, that theimplementation of the risk reduction program will provide. The user (20)can optionally edit or confirm the purchase order, increase the amountthat can be spent on risk reduction or chose to purchase a mix that isnot the optimal mix. After the user (20) completes his or her review andoptional edit, the software in block 514 transmits any orders topurchase the risk reduction products that were approved via the network(45). The software at this point could, of course, initialize one ormore bots to search the various web sites and exchanges to get the bestprice for the company using the system of the present invention. In anyevent, the details of the purchase transaction and confirmation are thensaved in the risk reduction purchase table (165) before processingadvances to block 515.

[0256] The software in block 515 displays the report selection window(705) to the user (20). The user (20) optionally selects reports forprinting. If the user (20) selects any reports for printing, then theinformation regarding the reports selected is saved in the reports table(164). After the user (20) has finished selecting reports, processingadvances to a software block 516.

[0257] The software in block 516 checks the reports tables (164) todetermine if any reports have been designated for printing. If reportshave been designated for printing, then processing advances to a block525. The software in block 525 sends the designated reports to theprinter (118). After the reports have been sent to the printer (118),processing advances to a software block 527. Alternatively, if noreports were designated for printing, then processing advances directlyfrom block 516 to block 527.

[0258] The software in block 527 checks the system settings table (140)to determine if the system is operating in a continuous run mode. If thesystem is operating in a continuous run mode, then processing returns toblock 205 and the processing described previously is repeated.Alternatively, if the system is not running in continuous mode, then theprocessing advances to a block 528 where the system stops.

[0259] While the above description contains many specificity's, theseshould not be construed as limitations on the scope of the invention,but rather as an exemplification of one embodiment thereof. Accordingly,the scope of the invention should be determined not by the embodimentillustrated, but by the appended claims and their legal equivalents.

1. A computer readable medium having sequences of instructions storedtherein, which when executed cause the processors in a plurality ofcomputers that have been connected via a network to perform a businessactivity management method, comprising: integrating organization relateddata, identifying transaction measures with impact on one or moreaspects of organization financial performance using at least a portionsaid data, modeling organization financial performance using saidmeasures as required to identify changes in transactions that willoptimize one or more aspects of organization financial performance, andimplementing changes in business activities that will generate saidchanges.
 2. The computer readable medium of claim 1 where the methodfurther comprises making the list of changes and business activitiesavailable for review and use via a paper document or electronic display.3. The computer readable medium of claim 1 where an organization is asingle product, a group of products, a division, a company, a multicompany corporation or a value chain.
 4. The computer readable medium ofclaim 1 where data is aggregated using a xml and a common schema.
 5. Thecomputer readable medium of claim 1 where organization related data isobtained from the group consisting of advanced financial systems, basicfinancial systems, web site management systems, alliance managementsystems, brand management systems, customer relationship managementsystems, channel management systems, intellectual property managementsystems, process management systems, vendor management systems,operation management systems, sales management systems, human resourcesystems, accounts receivable systems, accounts payable systems, capitalasset systems, inventory systems, invoicing systems, payroll systems,enterprise resource planning systems (ERP), material requirementplanning systems (MRP), scheduling systems, quality control systems,purchasing systems, risk management systems, the Internet, externaldatabases, user input and combinations thereof.
 6. The computer readablemedium of claim 1 where the transaction measures are selected from thegroup consisting of transaction ratios, time lagged transaction ratios,transaction trends, time lagged transaction trends, transactionaverages, time lagged transaction averages, transaction data, timelagged transaction data, transaction patterns, time lagged transactionpatterns, geospatial transaction measures, time lagged geospatialtransaction measures, relative rankings, time lagged relative rankings,internet links, time lagged internet links, transaction frequencies,time lagged transaction frequencies, transaction time periods, timelagged transaction time periods, average transaction time periods, timelagged average transaction time periods, cumulative transaction timeperiods, cumulative transaction time periods, rolling averagetransaction time period, rolling average transaction time period,cumulative total transactions, time lagged cumulative totaltransactions, period to period rate of change in transactions, timelagged period to period rate of change in transactions and combinationsthereof.
 7. The computer readable medium of claim 1 where thetransaction measures are identified by element of value where theelements of value are selected from the group consisting of alliances,brands, channels, customers, customer relationships, employees,intellectual capital, intellectual property, partnerships, processes,production equipment, vendors, vendor relationships and combinationsthereof.
 8. The computer readable medium of claim 1 where thetransaction measures are identified by category of value where thecategories of value are selected from the group consisting of currentoperation, real options, market sentiment and combinations thereof. 9.The computer readable medium of claim 1 where the one or more aspects oforganization financial performance are selected from the groupconsisting of revenue, expense, capital change, current operation value,real option value, market sentiment value, market value, alliance value,brand value, channel value, customer value, customer relationship value,employee value, intellectual capital value, intellectual property value,partnership value, process value, production equipment value, vendorvalue, vendor relationship value, current operation risk, real optionrisk, market sentiment risk, market risk, alliance risk, brand risk,channel risk, customer risk, customer relationship risk, employee risk,intellectual capital risk, intellectual property risk, partnership risk,process risk, production equipment risk, vendor risk, vendorrelationship risk, share price and combinations thereof.
 10. Thecomputer readable medium of claim 1 where the changes in businessactivities are selected from the group consisting of changes in purchasequantities, changes in purchasing mix, changes in vendors, changes inpurchase discounts, changes in product discounts, changes in productpricing, changes in service pricing, changes in service discounts,changes in supply chain management, changes in the organization equityholdings, changes in operating limits for organization systems, changesin process management and combinations thereof.
 11. A business activitymanagement system, comprising: a plurality of computers connected by anetwork each with a processor having circuitry to execute instructions;a storage device available to each processor with sequences ofinstructions stored therein, which when executed cause the processorsto: integrate organization related data, identify tangible measures ofelement and external factor impact on one or more aspects oforganization financial performance using at least a portion said data,model organization financial performance using said measures as requiredto identify changes by element and external factor that will optimizeone or more aspects of organization financial performance, make the listof changes available for review and optional approval via a paperdocument or electronic display, and implement one or more changes inbusiness activities that will generate said changes.
 12. The system ofclaim 11 where the method further comprises making the list of changesand business activities available for review and use via a paperdocument or electronic display.
 13. The system of claim 11 where anenterprise is a single product, a group of products, a division, acompany, a multi company corporation or a value chain.
 14. The system ofclaim 11 where data is integrated using xml and a common schema.
 15. Thesystem of claim 11 where organization related data is obtained from thegroup consisting of advanced financial systems, basic financial systems,web site management systems, alliance management systems, brandmanagement systems, customer relationship management systems, channelmanagement systems, intellectual property management systems, processmanagement systems, vendor management systems, operation managementsystems, sales management systems, human resource systems, accountsreceivable systems, accounts payable systems, capital asset systems,inventory systems, invoicing systems, payroll systems, enterpriseresource planning systems (ERP), material requirement planning systems(MRP), scheduling systems, quality control systems, purchasing systems,risk management systems, the Internet, external databases, user inputand combinations thereof.
 16. The system of claim 11 where the measuresare selected from the group consisting of transaction ratios, timelagged transaction ratios, transaction trends, time lagged transactiontrends, transaction averages, time lagged transaction averages, timelagged transaction data, transaction patterns, time lagged transactionpatterns, geospatial transaction measures, time lagged geospatialtransaction measures, relative rankings, time lagged relative rankings,internet links, time lagged internet links, transaction frequencies,time lagged transaction frequencies, transaction time periods, timelagged transaction time periods, average transaction time periods, timelagged average transaction time periods, cumulative transaction timeperiods, cumulative transaction time periods, rolling averagetransaction time period, rolling average transaction time period,cumulative total transactions, time lagged cumulative totaltransactions, period to period rate of change in transactions, timelagged period to period rate of change in transactions, compositevariables, vectors and combinations thereof.
 17. The system of claim 11where the measures are identified by category of value where categoriesof value are selected from the group consisting of current operations,real options, market sentiment and combinations thereof.
 18. The systemof claim 11 where the one or more aspects of organization financialperformance are selected from the group consisting of revenue, expense,capital change, current operation value, real option value, marketsentiment value, market value, alliance value, brand value, channelvalue, customer value, customer relationship value, employee value,intellectual capital value, intellectual property value, partnershipvalue, process value, production equipment value, vendor value, vendorrelationship value, current operation risk, real option risk, marketsentiment risk, market risk, alliance risk, brand risk, channel risk,customer risk, customer relationship risk, employee risk, fire risk,earthquake risk, flood risk, weather risk, contingent liabilities,intellectual capital risk, intellectual property risk, partnership risk,process risk, production equipment risk, vendor risk, vendorrelationship risk, share price and combinations thereof.
 19. The systemof claim 11 where changes in business activities are selected from thegroup consisting of changes in purchase quantities;, changes inpurchasing mix, changes in vendors, changes in purchase discounts,changes in product discounts, changes in product pricing, changes inservice pricing, changes in service discounts, changes in supply chainmanagement, changes in the organization equity holdings, changes inoperating limits for organization systems, changes in processmanagement, changes in risk transfer purchases and combinations thereof.20. An interactive financial model that identifies the contribution ofbusiness activity and external factors to organization share price. 21.The model of claim 20 where the contribution of business activity ismodeled with one or more transaction metrics.
 22. The model of claim 21where the one or more transaction metrics are selected from the groupconsisting of transaction ratios, time lagged transaction ratios,transaction trends, time lagged transaction trends, transactionaverages, time lagged transaction averages, time lagged transactiondata, transaction patterns, time lagged transaction patterns, geospatialtransaction measures, time lagged geospatial transaction measures,relative rankings, time lagged relative rankings, internet links, timelagged internet links, transaction frequencies, time lagged transactionfrequencies, transaction time periods, time lagged transaction timeperiods, average transaction time periods, time lagged averagetransaction time periods, cumulative transaction time periods,cumulative transaction time periods, rolling average transaction timeperiod, rolling average transaction time period, cumulative totaltransactions, time lagged cumulative total transactions, period toperiod rate of change in transactions, time lagged period to period rateof change in transactions, composite variables, vectors and combinationsthereof.
 23. The model of claim 21 where the transaction metrics are keyperformance indicators.
 24. The model of claim 20 where the contributionof external factors is determined using one or more tangible measures ofexternal factor impact on aspects of financial performance.
 25. Themodel of claim 20 that identifies the value of each of one or moreorganization elements of value is determined by its net contribution toorganization value.
 26. The model of claim 25 where the net contributionof an element to organization value is its direct contribution toorganization value net of any contribution to the other elements ofvalue and external factors that impact organization value.
 27. The modelof claim 25 where the elements of value are selected from the groupconsisting of alliances, brands, channels, customers, customerrelationships, employees, intellectual capital, intellectual property,partnerships, processes, production equipment, vendors, vendorrelationships and combinations thereof.
 28. The model of claim 27 wherebrands are selected from the group consisting of a symbol indicatingownership, a symbol indicating source, a device indicating ownership, adevice indicating source, mark, hallmark, label, logo, logotype, trademark, stamp, tag, seal, a distinctive style, model, cut, line, make,pattern, a specific characteristic ascribed to an organization, aspecific characteristic ascribed to an organization offering, a specificreputation ascribed to an organization, a specific reputation ascribedto an organization offering, a specific trait ascribed to anorganization, a specific trait ascribed to an organization offering andcombinations thereof.
 29. The model of claim 27 where processes areselected from the group consisting of a series of actions bring about aresult, a series changes bringing about a result, a series of functionsbringing about a result and combinations thereof.
 30. The model of claim20 that supports the optimization of one or more aspects of organizationfinancial performance where the one or more aspects of financialperformance are selected from the group consisting of revenue, expense,capital change, current operation value, real option value, marketsentiment value, market value, alliance value, brand value, channelvalue, customer value, customer relationship value, employee value,intellectual capital value, intellectual property value, partnershipvalue, process value, production equipment value, vendor value, vendorrelationship value, current operation risk, real option risk, marketsentiment risk, market risk, alliance risk, brand risk, channel risk,customer risk, customer relationship risk, employee risk, fire risk,earthquake risk, flood risk, weather risk, contingent liabilities,intellectual capital risk, intellectual property risk, partnership risk,process risk, production equipment risk, vendor risk, vendorrelationship risk and combinations thereof.
 31. The model of claim 20that supports the optimization of business activities where businessactivities are selected from the group consisting of purchasing,pricing, process management, supply chain management, risk management,sales, stock price management and combinations thereof.
 32. The model ofclaim 31 where purchasing activities are selected from the groupconsisting of changes in purchase quantities, changes in purchasing mix,changes in vendors, changes in purchase discounts, changes in purchaseprices, changes in purchase frequency and combinations thereof.
 33. Amethod for integrating organization systems into an overall financialmanagement system, comprising integrating data from organization relatedsystems using xml and a common schema, developing a model oforganization share price using at least a portion of said data,identifying changes in operation that will optimize one or more driversof organization share price using said model, and implementing saidchanges in operation by communicating the changes to one or moreorganization systems.
 34. The method of claim 33 where organizationrelated systems are selected from the group consisting of advancedfinancial systems, basic financial systems, web site management systems,alliance management systems, brand management systems, customerrelationship management systems, channel management systems,intellectual property management systems, process management systems,vendor management systems, operation management systems, salesmanagement systems, human resource systems, accounts receivable systems,accounts payable systems, capital asset systems, inventory systems,invoicing systems, payroll systems, enterprise resource planning systems(ERP), material requirement planning systems (MRP), scheduling systems,quality control systems, purchasing systems, risk management systems,and combinations thereof.
 35. The system of claim 33 that where changesin operation are selected from the group consisting of changes inpurchasing, changes in pricing, changes in processes, changes in thesupply chain, changes in risk management, changes in sales, changes inequity holdings and combinations thereof.